Engineering › Aerospace Engineering

Advanced SAR Imaging Techniques

Works Count: 30531

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Description

This cluster of papers covers a wide range of topics related to Synthetic Aperture Radar (SAR) technology and its applications. It includes research on Micro-Doppler effects, target classification using deep learning techniques, automatic target recognition, signal processing methods, Doppler radar for human activity detection, motion compensation algorithms, and the use of Convolutional Neural Networks for SAR image analysis.

Keywords

Synthetic Aperture Radar; Micro-Doppler Effect; Target Classification; Convolutional Neural Networks; Automatic Target Recognition; Signal Processing; Doppler Radar; Motion Compensation; Deep Learning; Bistatic SAR

Spotlight-Mode Synthetic Aperture Radar: A Signal Processing Approach

1996-01-01

Charles V. Jakowatz , Daniel E. Wahl , Paul H. Eichel +2 more

Spotlight synthetic aperture radar : signal processing algorithms

1995-01-01

Walter G. Carrara , Ron Goodman , R. M. Majewski

Part 1 Introduction: spotlight SAR SAR modes importance of spotlight SAR early SAR chronology. Part 2 Synthetic aperture radar fundamentals: SAR system overview imaging considerations pulse compression and range resolution … Part 1 Introduction: spotlight SAR SAR modes importance of spotlight SAR early SAR chronology. Part 2 Synthetic aperture radar fundamentals: SAR system overview imaging considerations pulse compression and range resolution synthetic aperture technique for Azimuth resolution SAR coherence requirements signal phase equation inverse SAR (ISAR) SAR sensor parametric design. Part 3 Spotlight SAR and polar format algorithm: scope of processing task polar format overview polar data storage as a two-dimensional signal correction for non-planar motion polar format algorithm limitations Taylor series expansion procedures phase of image pixels image geometric distortion image focus error equations displacements and absolute positioning. Part 4 Digital polar format processing: sampling rate conversion polyphase filters polar interpolation image scale factors image distortion correction signal history projections stabilized scene polar interpolation subpatch processing and mosaicking. Part 5 Phase errors: classification of phase error management of phase error magnitude of phase error requirements on a practical SAR motion sensor moving target effects. Part 6 Autofocus techniques: mapdrift multiple aperture mapdrift phase difference phase gradient prominent point processing considerations for space-variant refocus. Part 7 Processor design examples: the common UNIX SAR processor the ground to air imaging radar processor. Part 8 SAR system performance: image quality metrics system performance budgeting requirements on system impulse response requirements on system noise geometric distortion secondary image quality metrics test arrays. Part 9 Spotlight processing applications: spotlight processing of scan and stripmap SAR data interferometric SAR forward look SAR vibrating target detection. Part 10 Range migration algorithm: model algorithm overview analytical development discussion efficient algorithms for range migration processing. Part 11 Chirp scaling algorithm: non-dechirped signal model algorithm overview analytical development discussion. Part 12 Comparison of image formation algorithms: image formation algorithm models computational complexity memory requirements other considerations.

Two-Dimensional Imaging

1999-01-01

R. N. Bracewell

1. Introduction. 2. The Image Plane. 3. Two Dimensional Impulses. 4. The Two Dimensional Fourier Transform. 5. Two Dimensional Convolution. 6. The Convolution Theorem. 7. Sampling and Interpolation in Two … 1. Introduction. 2. The Image Plane. 3. Two Dimensional Impulses. 4. The Two Dimensional Fourier Transform. 5. Two Dimensional Convolution. 6. The Convolution Theorem. 7. Sampling and Interpolation in Two Dimensions. 8. Digital Operations. 9. Rotational Symmetry and the Two Dimensional Fourier Transform. 10. Imaging by Convolution. 11. Diffraction Theory of Sensors. 12. Indirect Imaging and Interferometry. 13. Restoration of Images. 14. The Projection-Slice Theorem. 15. Computed Tomography. 16. Synthetic Aperture Radar. 17. Random Images and Fractals. Index.

An adaptive filtering approach to spectral estimation and SAR imaging

1996-06-01

Jian Li , Petre Stoica

We present an adaptive FIR filtering approach, which is referred to as the amplitude and phase estimation of a sinusoid (APES), for complex spectral estimation. We compare the APES algorithm … We present an adaptive FIR filtering approach, which is referred to as the amplitude and phase estimation of a sinusoid (APES), for complex spectral estimation. We compare the APES algorithm with other FIR filtering approaches including the Welch (1967) and Capon (1969) methods. We also describe how to apply the FIR filtering approaches to target range signature estimation and synthetic aperture radar (SAR) imaging. We show via both numerical and experimental examples that the adaptive FIR filtering approaches such as Capon and APES can yield more accurate spectral estimates with much lower sidelobes and narrower spectral peaks than the FFT method, which is also a special case of the FIR filtering approach. We show that although the APES algorithm yields somewhat wider spectral peaks than the Capon method, the former gives more accurate overall spectral estimates and SAR images than the latter and the FFT method.

Target-Motion-Induced Radar Imaging

1980-01-01

Chung-Ching Chen , Harry Andrews

Imaging from ground-based (stationary) radars of moving targets is often possible by utilizing a "synthetic aperture" developed from the target motion itself. The theory and experimental results associated with such … Imaging from ground-based (stationary) radars of moving targets is often possible by utilizing a "synthetic aperture" developed from the target motion itself. The theory and experimental results associated with such processing are addressed. An aircraft is imaged from both a straight flight and a turn with recognizable results. Analysis shows that two-phase components exist in the radar return, one being gross velocity induced, the other being interscatterer interference within the target itself. The former phase must be removed prior to imaging and techniques are developed for this task. Preprocessing, range curvature, range alignment, motion compensation, and presumming are all addressed prior to presenting the experimental results. Coherence processing intervals, range collapsing, and range realignment are all examined during the processing aspects of the paper.

A tomographic formulation of spotlight-mode synthetic aperture radar

1983-01-01

D.C. Munson , James D. O'Brien , W.K. Jenkins

Spotlight-mode synthetic aperture radar (spotlight-mode SAR) synthesizes high-resolution terrain maps using data gathered from multiple observation angles. This paper shows that spotlight-mode SAR can be interpreted as a tomographic reeonstrution … Spotlight-mode synthetic aperture radar (spotlight-mode SAR) synthesizes high-resolution terrain maps using data gathered from multiple observation angles. This paper shows that spotlight-mode SAR can be interpreted as a tomographic reeonstrution problem and analyzed using the projection-slice theorem from computer-aided tomograpy (CAT). The signal recorded at each SAR transmission point is modeled as a portion of the Fourier transform of a central projection of the imaged ground area. Reconstruction of a SAR image may then be accomplished using algorithms from CAT. This model permits a simple understanding of SAR imaging, not based on Doppler shifts. Resolution, sampling rates, waveform curvature, the Doppler effect, and other issues are also discussed within the context of this interpretation of SAR.

A Two-Dimensional Spectrum for Bistatic SAR Processing Using Series Reversion

2007-01-01

Yew Lam Neo , F.H. Wong , Ian Cumming

This letter derives the two-dimensional point target spectrum for an arbitrary bistatic synthetic aperture radar configuration. The method described makes use of series reversion, the method of stationary phase, and … This letter derives the two-dimensional point target spectrum for an arbitrary bistatic synthetic aperture radar configuration. The method described makes use of series reversion, the method of stationary phase, and Fourier transform pairs to derive the point target spectrum. The accuracy of the spectrum is controlled by keeping enough terms in the two series expansions, and is verified with a point target simulation

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Analysis of micro-Doppler signatures

2003-01-01

V.C. Chen , F. Li , Shen-Shyang Ho +1 more

Mechanical vibration or rotation of a target or structures on the target may induce additional frequency modulations on the returned radar signal which generate sidebands about the target's Doppler frequency, … Mechanical vibration or rotation of a target or structures on the target may induce additional frequency modulations on the returned radar signal which generate sidebands about the target's Doppler frequency, called the micro-Doppler effect. Micro-Doppler signatures enable some properties of the target to be determined. In the paper, the micro-Doppler effect in radar is introduced and the mathematics of micro-Doppler signatures is developed. Computer simulations are conducted and micro-Doppler features in the joint time–frequency domain are exploited.

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Multidimensional Waveform Encoding: A New Digital Beamforming Technique for Synthetic Aperture Radar Remote Sensing

2007-12-20

Gerhard Krieger , Nicolas Gebert , Alberto Moreira

This paper introduces the innovative concept of multidimensional waveform encoding for spaceborne synthetic aperture radar (SAR). The combination of this technique with digital beamforming on receive enables a new generation … This paper introduces the innovative concept of multidimensional waveform encoding for spaceborne synthetic aperture radar (SAR). The combination of this technique with digital beamforming on receive enables a new generation of SAR systems with improved performance and flexible imaging capabilities. Examples are high-resolution wide-swath radar imaging with compact antennas, enhanced sensitivity for applications like alongtrack interferometry and moving object indication, and the implementation of hybrid SAR imaging modes that are well suited to satisfy hitherto incompatible user requirements. Implementation-specific issues are discussed and performance examples demonstrate the potential of the new technique for different remote sensing applications.

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Phase gradient autofocus-a robust tool for high resolution SAR phase correction

1994-07-01

Daniel E. Wahl , Paul H. Eichel , Dennis C. Ghiglia +1 more

The phase gradient autofocus (PGA) technique for phase error correction of spotlight mode synthetic aperture radar (SAR) imagery is examined carefully in the context of four fundamental signal processing steps … The phase gradient autofocus (PGA) technique for phase error correction of spotlight mode synthetic aperture radar (SAR) imagery is examined carefully in the context of four fundamental signal processing steps that constitute the algorithm. We demonstrate that excellent results over a wide variety of scene content, and phase error function structure are obtained if and only if all of these steps are included in the processing. Finally, we show that the computational demands of the fun PGA algorithm do not represent a large fraction of the total image formation problem, when mid to large size images are involved.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

<title>Standard SAR ATR evaluation experiments using the MSTAR public release data set</title>

1998-09-15

Timothy D. Ross , Steven W. Worrell , Vincent J. Velten +2 more

The recent public release of high resolution Synthetic Aperture Radar (SAR) data collected by the DARPA/AFRL Moving and Stationary Target Acquisition and Recognition (MSTAR) program has provided a unique opportunity … The recent public release of high resolution Synthetic Aperture Radar (SAR) data collected by the DARPA/AFRL Moving and Stationary Target Acquisition and Recognition (MSTAR) program has provided a unique opportunity to promote and assess progress in SAR ATR algorithm development. This paper will suggest general principles to follow and report on a specific ATR performance experiment using these principles and this data. The principles and experiments are motivated by AFRL experience with the evaluation of the MSTAR ATR.

Maneuvering Target Detection via Radon-Fractional Fourier Transform-Based Long-Time Coherent Integration

2014-01-24

Xiaolong Chen , Jian Guan , Ningbo Liu +1 more

Long-time coherent integration technique is one of the most important methods for the improvement of radar detection ability of a weak maneuvering target, whereas the integration performance may be greatly … Long-time coherent integration technique is one of the most important methods for the improvement of radar detection ability of a weak maneuvering target, whereas the integration performance may be greatly influenced by the across range unit (ARU) and Doppler frequency migration (DFM) effects. In this paper, a novel representation known as Radon-fractional Fourier transform (RFRFT) is proposed and investigated to solve the above problems simultaneously. It can not only eliminate the effect of DFM by selecting a proper rotation angle but also achieve long-time coherent integration without ARU effect. The RFRFT can be regarded as a special Doppler filter bank composed of filters with different rotation angles, which indicates a generalization of the traditional moving target detection (MTD) and FRFT methods. Some useful properties and the likelihood ratio test detector of RFRFT are derived for maneuvering target detection. Finally, numerical experiments of aerial target and marine target detection are carried out using simulated and real radar datasets. The results demonstrate that for integration gain and detection ability, the proposed method is superior to MTD, FRFT, and Radon-Fourier transform under low signal-to-clutter/noise ratio (SCR/SNR) environments. Moreover, the trajectory of target can be easily obtained via RFRFT as well.

Airborne SAR processing of highly squinted data using a chirp scaling approach with integrated motion compensation

1994-01-01

Alberto Moreira , Yonghong Huang

Proposes a new approach for high-resolution airborne SAR data processing, which uses a modified chirp scaling algorithm to accommodate the correction of motion errors, as well as the variations of … Proposes a new approach for high-resolution airborne SAR data processing, which uses a modified chirp scaling algorithm to accommodate the correction of motion errors, as well as the variations of the Doppler centroid in range and azimuth. By introducing a cubic phase term in the chirp scaling phase, data acquired with a squint angle up to 30/spl deg/ can be processed with no degradation of the impulse response function. The proposed approach is computationally very efficient, since it accommodates the variations of Doppler centroid without using block processing. Furthermore, a motion error extraction algorithm can be incorporated into the proposed approach by means of subaperture processing in azimuth. The new approach, denoted as extended chirp scaling, is considered to be a generalized algorithm suitable for the high-resolution processing of most airborne SAR systems.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Estimating the Doppler centroid of SAR data

1989-03-01

S.N. Madsen

After reviewing frequency-domain techniques for estimating the Doppler centroid of synthetic-aperture radar (SAR) data, the author describes a time-domain method and highlights its advantages. In particular, a nonlinear time-domain algorithm … After reviewing frequency-domain techniques for estimating the Doppler centroid of synthetic-aperture radar (SAR) data, the author describes a time-domain method and highlights its advantages. In particular, a nonlinear time-domain algorithm called the sign-Doppler estimator (SDE) is shown to have attractive properties. An evaluation based on an existing SEASAT processor is reported. The time-domain algorithms are shown to be extremely efficient with respect to requirements on calculations and memory, and hence they are well suited to real-time systems where the Doppler estimation is based on raw SAR data. For offline processors where the Doppler estimation is performed on processed data, which removes the problem of partial coverage of bright targets, the Delta E estimator and the CDE (correlation Doppler estimator) algorithm give similar performance. However, for nonhomogeneous scenes it is found that the nonlinear SDE algorithm, which estimates the Doppler-shift on the basis of data signs alone, gives superior performance.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

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Precision SAR processing using chirp scaling

1994-07-01

R. K. Raney , Hartmut Runge , Richard Bamler +2 more

A space-variant interpolation is required to compensate for the migration of signal energy through range resolution cells when processing synthetic aperture radar (SAR) data, using either the classical range/Doppler (R/D) … A space-variant interpolation is required to compensate for the migration of signal energy through range resolution cells when processing synthetic aperture radar (SAR) data, using either the classical range/Doppler (R/D) algorithm or related frequency domain techniques. In general, interpolation requires significant computation time, and leads to loss of image quality, especially in the complex image. The new chirp scaling algorithm avoids interpolation, yet performs range cell migration correction accurately. The algorithm requires only complex multiplies and Fourier transforms to implement, is inherently phase preserving, and is suitable for wide-swath, large-beamwidth, and large-squint applications. This paper describes the chirp scaling algorithm, summarizes simulation results, presents imagery processed with the algorithm, and reviews quantitative measures of its performance. Based on quantitative comparison, the chirp scaling algorithm provides image quality equal to or better than the precision range/Doppler processor. Over the range of parameters tested, image quality results approach the theoretical limit, as defined by the system bandwidth.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

MIMO-SAR: Opportunities and Pitfalls

2013-06-28

Gerhard Krieger

This paper reviews advanced radar architectures that employ multiple transmit and multiple receive antennas to improve the performance of future synthetic aperture radar (SAR) systems. These advanced architectures have been … This paper reviews advanced radar architectures that employ multiple transmit and multiple receive antennas to improve the performance of future synthetic aperture radar (SAR) systems. These advanced architectures have been dubbed multiple-input multiple-output SAR (MIMO-SAR) in analogy to MIMO communication systems. Considerable confusion arose, however, with regard to the selection of suitable waveforms for the simultaneous transmission via multiple channels. It is shown that the mere use of orthogonal waveforms is insufficient for the desired performance improvement in view of most SAR applications. As a solution to this fundamental MIMO-SAR challenge, a new class of short-term shift-orthogonal waveforms is introduced. The short-term shift orthogonality avoids mutual interferences from the radar echoes of closely spaced scatterers, while interferences from more distant scatterers are suppressed by digital beamforming on receive in elevation. Further insights can be gained by considering the data acquisition of a side-looking imaging radar in a 3-D information cube. It becomes evident that the suggested waveforms fill different subspaces that can be individually accessed by a multichannel receiver. For completeness, the new class of short-term shift-orthogonal waveforms is also compared to a recently proposed pair of orthogonal frequency-division multiplexing waveforms. It is shown that both sets of waveforms require essentially the same principle of range time to elevation angle conversion via a multichannel receiver in order to be applicable for MIMO-SAR imaging without interference.

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Tutorial review of synthetic-aperture radar (SAR) with applications to imaging of the ocean surface

1978-01-01

K. Tomiyasu

A synthetic aperture radar (SAR) can produce high-resolution two-dimensional images of mapped areas. The SAR comprises a pulsed transmitter, an antenna, and a phase-coherent receiver. The SAR is borne by … A synthetic aperture radar (SAR) can produce high-resolution two-dimensional images of mapped areas. The SAR comprises a pulsed transmitter, an antenna, and a phase-coherent receiver. The SAR is borne by a constant velocity vehicle such as an aircraft or satellite, with the antenna beam axis oriented obliquely to the velocity vector. The image plane is defined by the velocity vector and antenna beam axis. The image orthogonal coordinates are range and cross range (azimuth). The amplitude and phase of the received signals are collected for the duration of an integration time after which the signal is processed. High range resolution is achieved by the use of wide bandwidth transmitted pulses. High azimuth resolution is achieved by focusing, with a signal processing technique, an extremely long antenna that is synthesized from the coherent phase history. The pulse repetition frequency of the SAR is constrained within bounds established by the geometry and signal ambiguity limits. SAR operation requires relative motion between radar and target. Nominal velocity values are assumed for signal processing and measurable deviations are used for error compensation. Residual uncertainties and high-order derivatives of the velocity which are difficult to compensate may cause image smearing, defocusing, and increased image sidelobes. The SAR transforms the ocean surface into numerous small cells, each with dimensions of range and azimuth resolution. An image of a cell can be produced provided the radar cross section of the cell is sufficiently large and the cell phase history is deterministic. Ocean waves evidently move sufficiently uniformly to produce SAR images which correlate well with optical photographs and visual observations. The relationship between SAR images and oceanic physical features is not completely understood, and more analyses and investigations are desired.

Feature-enhanced synthetic aperture radar image formation based on nonquadratic regularization

2001-04-01

Müjdat Çetin , W.C. Karl

We develop a method for the formation of spotlight-mode synthetic aperture radar (SAR) images with enhanced features. The approach is based on a regularized reconstruction of the scattering field which … We develop a method for the formation of spotlight-mode synthetic aperture radar (SAR) images with enhanced features. The approach is based on a regularized reconstruction of the scattering field which combines a tomographic model of the SAR observation process with prior information regarding the nature of the features of interest. Compared to conventional SAR techniques, the method we propose produces images with increased resolution, reduced sidelobes, reduced speckle and easier-to-segment regions. Our technique effectively deals with the complex-valued, random-phase nature of the underlying SAR reflectivities. An efficient and robust numerical solution is achieved through extensions of half-quadratic regularization methods to the complex-valued SAR problem. We demonstrate the performance of the method on synthetic and real SAR scenes.

The delay/Doppler radar altimeter

1998-01-01

R. K. Raney

The key innovation in the delay/Doppler radar altimeter is delay compensation, analogous to range curvature correction in a burst-mode synthetic aperture radar (SAR). Following delay compensation, height estimates are sorted … The key innovation in the delay/Doppler radar altimeter is delay compensation, analogous to range curvature correction in a burst-mode synthetic aperture radar (SAR). Following delay compensation, height estimates are sorted by Doppler frequency, and integrated in parallel. More equivalent looks are accumulated than in a conventional altimeter. The relatively small along-track footprint size is a constant of the system, typically on the order of 250 m for a Ku-band altimeter. The flat-surface response is an impulse rather than the more familiar step function produced by conventional satellite radar altimeters. The radar equation for the delay/Doppler radar altimeter has an h/sup -5/2/(CT)/sup 1/2/ dependence on height h and compressed pulse length /spl tau/, which is more efficient than the corresponding h/sup 3/CT factor for a pulse-limited altimeter. The radiometric response obtained by the new approach would be 10 dB stronger than that of the TOPEX/Poseidon altimeter, for example, if the same hardware were used in the delay/Doppler altimeter mode. This new technique leads to a smaller instrument that requires less power, yet performs better than a conventional radar altimeter. The concept represents a new generation of altimeter for Earth observation, with particular suitability for coastal ocean regions and polar ice sheets as well as open oceans.

Spotlight SAR data processing using the frequency scaling algorithm

1999-01-01

Josef Mittermayer , Alberto Moreira , Otmar Loffeld

This paper presents a new processing algorithm for spotlight SAR data processing. The algorithm performs the range cell migration correction for non-chirped raw data without interpolation by using a novel … This paper presents a new processing algorithm for spotlight SAR data processing. The algorithm performs the range cell migration correction for non-chirped raw data without interpolation by using a novel frequency scaling operation. The azimuth processing is based on a spectral analysis approach which is made highly accurate by azimuth scaling. In almost all processing stages, a subaperture approach is introduced for efficient azimuth processing. In this paper, the complete derivation of the algorithm is presented. A very useful formulation for non-chirped SAR signals in the range Doppler domain is also proposed where the residual video phase is expressed by a chirp convolution. The algorithm performance is shown by several simulations. A spotlight image, which has been extracted from stripmap raw data of the experimental SAR system of DLR, shows the validity of the frequency scaling algorithm.

Synthetic-aperture radar processing using fast factorized back-projection

2003-07-01

Lars M. H. Ulander , Hans Hellsten , G. Stenström

Exact synthetic aperture radar (SAR) inversion for a linear aperture may be obtained using fast transform techniques. Alternatively, back-projection integration in time domain can also be used. This technique has … Exact synthetic aperture radar (SAR) inversion for a linear aperture may be obtained using fast transform techniques. Alternatively, back-projection integration in time domain can also be used. This technique has the benefit of handling a general aperture geometry. In the past, however, back-projection has seldom been used due to heavy computational burden. We show that the back-projection integral can be recursively partitioned and an effective algorithm constructed based on aperture factorization. By representing images in local polar coordinates it is shown that the number of operations is drastically reduced and can be made to approach that of fast transform algorithms. The algorithm is applied to data from the airborne ultra-wideband CARABAS SAR and shown to give a reduction in processing time of two to three orders of magnitude.

Unambiguous SAR Signal Reconstruction From Nonuniform Displaced Phase Center Sampling

2004-10-01

Gerhard Krieger , Nicolas Gebert , Alberto Moreira

The displaced phase center (DPC) technique will enable a wide-swath synthetic aperture radar (SAR) with high azimuth resolution. In a classic DPC system, the pulse repetition frequency (PRF) has to … The displaced phase center (DPC) technique will enable a wide-swath synthetic aperture radar (SAR) with high azimuth resolution. In a classic DPC system, the pulse repetition frequency (PRF) has to be chosen such that the SAR carrier moves just one half of its antenna length between subsequent radar pulses. Any deviation from this PRF will result in a nonuniform sampling of the synthetic aperture. This letter derives an innovative reconstruction algorithm and shows that an unambiguous reconstruction of a SAR signal is possible for nonuniform sampling of the synthetic aperture. This algorithm will also have great potential for multistatic satellite constellations as well as the dual receive antenna mode in Radarsat 2 and TerraSAR-X.

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SARAS: a synthetic aperture radar (SAR) raw signal simulator

1992-01-01

G. Franceschetti , Maurizio Migliaccio , Daniele Riccio +1 more

An SAR simulator of an extended three-dimensional scene is presented. It is based on a facet model for the scene, asymptotic evaluation of SAR unit response, and a two-dimensional fast … An SAR simulator of an extended three-dimensional scene is presented. It is based on a facet model for the scene, asymptotic evaluation of SAR unit response, and a two-dimensional fast Fourier transform code for the data processing. Prescribed statistics of the model account for a realistic speckle of the image. The simulator is implemented in Synthetic Aperture Radar Advance Simulators (SARAS), whose performance is described and illustrated by a number of examples.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">></ETX>

Product high-order ambiguity function for multicomponent polynomial-phase signal modeling

1998-03-01

Sergio Barbarossa , Anna Scaglione , Georgios B. Giannakis

Parameter estimation and performance analysis issues are studied for multicomponent polynomial-phase signals (PPSs) embedded in white Gaussian noise. Identifiability issues arising with existing approaches are described first when dealing with … Parameter estimation and performance analysis issues are studied for multicomponent polynomial-phase signals (PPSs) embedded in white Gaussian noise. Identifiability issues arising with existing approaches are described first when dealing with multicomponent PPS having the same highest order phase coefficients. This situation is encountered in applications such as synthetic aperture radar imaging or propagation of polynomial phase signals through channels affected by multipath and is thus worthy of a careful analysis. A new approach is proposed based on a transformation called product high-order ambiguity function (PHAF). The use of the PHAF offers a number of advantages with respect to the high-order ambiguity function (HAF). More specifically, it removes the identifiability problem and improves noise rejection capabilities. Performance analysis is carried out using the perturbation method and verified by simulation results.

Radon-Fourier Transform for Radar Target Detection, I: Generalized Doppler Filter Bank

2011-04-01

Jia Xu , Ji Yu , Yingning Peng +1 more

Based on the coupling relationship among radial velocity, range walk, and Doppler frequency of the moving target's echoes, a novel method is proposed, i.e., Radon-Fourier transform (RFT), to realize the … Based on the coupling relationship among radial velocity, range walk, and Doppler frequency of the moving target's echoes, a novel method is proposed, i.e., Radon-Fourier transform (RFT), to realize the long-time coherent integration for radar target detection. The RFT realizes the echoes spatial-temporal decoupling via joint searching along range and velocity directions, as well as the successive coherent integration via the Doppler filter bank. Besides, four equivalent RFTs are obtained with respect to the different searching parameters. Furthermore, a generalized form of RFT, i.e., generalized Radon-Fourier transform (GRFT), is also defined for target detection with arbitrary parameterized motion. Due to the similarity between the RFT and the well-known moving target detection (MTD) method, this paper provides detailed comparisons between them on five aspects, i.e., coherent integration time, filter bank structure, blind speed response, detection performance, and computational complexity. It is shown that MTD is actually a special case of RFT and RFT is a kind of generalized Doppler filter bank processing for targets with across range unit (ARU) range walk. Finally, numerical experiments are provided to demonstrate the equivalence among four kinds of RFTs. Also, it is shown that the RFT may obtain the coherent integration gain in the different noisy background and the target's blind speed effect may be effectively suppressed. In the meantime, both the weak target detection performance and the radar coverage of high-speed targets may be significantly improved via RFT without change of the radar hardware system.

Developments in Radar Imaging

1984-07-01

D. A. Ausherman , A. Kozma , Jack L. Walker +2 more

Using range and Doppler information to produce radar images is a technique used in such diverse fields as air-to-ground imaging of objects, terrain, and oceans and ground-to-air imaging of aircraft, … Using range and Doppler information to produce radar images is a technique used in such diverse fields as air-to-ground imaging of objects, terrain, and oceans and ground-to-air imaging of aircraft, space objects, and planets. A review of the range-Doppler technique is presented along with a description of radar imaging forms including details of data acquisition and processing techniques.

SAR imaging of moving targets

1999-01-01

R. P. Perry , R.C. DiPietro , Ronald L. Fante

A method of forming synthetic aperture radar (SAR) images of moving targets without using any specific knowledge of the target motion is presented. The new method uses a unique processing … A method of forming synthetic aperture radar (SAR) images of moving targets without using any specific knowledge of the target motion is presented. The new method uses a unique processing kernel that involves a one-dimensional interpolation of the deramped phase history which we call keystone formatting. This preprocessing simultaneously eliminates the effects of linear range migration for all moving targets regardless of their unknown velocity. Step two of the moving target imaging technique involves a two-dimensional focusing of the movers to remove residual quadratic range migration errors. The third and last step removes cubic and higher order defocusing terms. This imaging technique is demonstrated using SAR data collected as part of DARPA's Moving Target Exploitation (MTE) program.

Signal Processing for FMCW SAR

2007-11-01

Adriano Meta , P. Hoogeboom , L.P. Ligthart

The combination of frequency-modulated continuous-wave (FMCW) technology and synthetic aperture radar (SAR) techniques leads to lightweight cost-effective imaging sensors of high resolution. One limiting factor to the use of FMCW … The combination of frequency-modulated continuous-wave (FMCW) technology and synthetic aperture radar (SAR) techniques leads to lightweight cost-effective imaging sensors of high resolution. One limiting factor to the use of FMCW sensors is the well-known presence of nonlinearities in the transmitted signal. This results in contrast- and range-resolution degradation, particularly when the system is intended for high-resolution long-range applications, as it is the case for SAR. This paper presents a novel processing solution, which solves the nonlinearity problem for the whole range profile. Additionally, the conventional stop-and-go approximation used in pulse-radar algorithms is not valid in FMCW SAR applications under certain circumstances. Therefore, the motion within the sweep needs to be taken into account. Analytical development of the FMCW SAR signal model, starting from the deramped signal and without using the stop-and-go approximation, is presented in this paper. The model is then applied to stripmap, spotlight, and single-transmitter/multiple-receiver digital-beamforming SAR operational mode. The proposed algorithms are verified by processing real FMCW SAR data collected with the demonstrator system built at the Delft University of Technology.

Autofocusing of ISAR images based on entropy minimization

1999-01-01

Xi Li , Liu Guosui , Jinlin Ni

A novel autofocusing technique is developed for random translational motion compensation in inverse synthetic aperture radar (ISAR) imaging of objects. This technique is based on an entropy minimization principle and … A novel autofocusing technique is developed for random translational motion compensation in inverse synthetic aperture radar (ISAR) imaging of objects. This technique is based on an entropy minimization principle and validated via a nonparametric estimation method. Images of a simulation and a real flying aircraft are used for illustration. Images of encouraging quality confirm the feasibility of autofocusing the radar images by just the requirement of minimizing the image entropy.

Joint time-frequency transform for radar range-Doppler imaging

1998-04-01

V.C. Chen , Shie Qian

Conventional radar imaging uses the Fourier transform to retrieve Doppler information. However, due to the complex motion of a target, the Doppler frequency shifts are actually time-varying. By using the … Conventional radar imaging uses the Fourier transform to retrieve Doppler information. However, due to the complex motion of a target, the Doppler frequency shifts are actually time-varying. By using the Fourier transform, the Doppler spectrum becomes smeared and the image is blurred. Without resorting to sophisticated motion compensation algorithms, the image blurring problem can be resolved with the joint time-frequency transform. High-resolution time-frequency transforms are investigated, and examples of applications to radar imaging of single and multiple targets with complex motion are given.

Human Activity Classification Based on Micro-Doppler Signatures Using a Support Vector Machine

2009-03-19

Youngwook Kim , Hao Ling

The feasibility of classifying different human activities based on micro-Doppler signatures is investigated. Measured data of 12 human subjects performing seven different activities are collected using a Doppler radar. The … The feasibility of classifying different human activities based on micro-Doppler signatures is investigated. Measured data of 12 human subjects performing seven different activities are collected using a Doppler radar. The seven activities include running, walking, walking while holding a stick, crawling, boxing while moving forward, boxing while standing in place, and sitting still. Six features are extracted from the Doppler spectrogram. A support vector machine (SVM) is then trained using the measurement features to classify the activities. A multiclass classification is implemented using a decision-tree structure. Optimal parameters for the SVM are found through a fourfold cross-validation. The resulting classification accuracy is found to be more than 90%. The potentials of classifying human activities over extended time duration, through wall, and at oblique angles with respect to the radar are also investigated and discussed.

Synthetic Aperture Imaging Radar and Moving Targets

1971-05-01

R. K. Raney

This paper considers the effects of slowly moving targets as they appear in the output of an airborne coherent side-looking synthetic aperture imaging radar. The image of a moving reflector … This paper considers the effects of slowly moving targets as they appear in the output of an airborne coherent side-looking synthetic aperture imaging radar. The image of a moving reflector is described, and two approaches to airborne moving target indication (AMTI) are summarized. It is shown that the effects of target movement are decreased as the radar scan rate is increased, and are increased as the (Doppler processed) compression ratio is increased.

Human Detection and Activity Classification Based on Micro-Doppler Signatures Using Deep Convolutional Neural Networks

2015-11-02

Youngwook Kim , Taesup Moon

We propose the use of deep convolutional neural networks (DCNNs) for human detection and activity classification based on Doppler radar. Previously, proposed schemes for these problems remained in the conventional … We propose the use of deep convolutional neural networks (DCNNs) for human detection and activity classification based on Doppler radar. Previously, proposed schemes for these problems remained in the conventional supervised learning paradigm that relies on the design of handcrafted features. Whereas these schemes attained high accuracy, the requirement for domain knowledge of each problem limits the scalability of the proposed schemes. In this letter, we present an alternative deep learning approach. We apply the DCNN, one of the most successful deep learning algorithms, directly to a raw micro-Doppler spectrogram for both human detection and activity classification problem. The DCNN can jointly learn the necessary features and classification boundaries using the measured data without employing any explicit features on the micro-Doppler signals. We show that the DCNN can achieve accuracy results of 97.6% for human detection and 90.9% for human activity classification.

Convolutional Neural Network With Data Augmentation for SAR Target Recognition

2016-01-01

Jun Ding , Bo Chen , Hongwei Liu +1 more

Many methods have been proposed to improve the performance of synthetic aperture radar (SAR) target recognition but seldom consider the issues in real-world recognition systems, such as the invariance under … Many methods have been proposed to improve the performance of synthetic aperture radar (SAR) target recognition but seldom consider the issues in real-world recognition systems, such as the invariance under target translation, the invariance under speckle variation in different observations, and the tolerance of pose missing in training data. In this letter, we investigate the capability of a deep convolutional neural network (CNN) combined with three types of data augmentation operations in SAR target recognition. Experimental results demonstrate the effectiveness and efficiency of the proposed method. The best performance is obtained by using the CNN trained by all types of augmentation operations, showing that it is a practical approach for target recognition in challenging conditions of target translation, random speckle noise, and missing pose.

Synthetic aperture radar — systems and signal processing

1992-10-01

Timo Lensu

Target Classification Using the Deep Convolutional Networks for SAR Images

2016-05-05

Sizhe Chen , Haipeng Wang , Feng Xu +1 more

The algorithm of synthetic aperture radar automatic target recognition (SAR-ATR) is generally composed of the extraction of a set of features that transform the raw input into a representation, followed … The algorithm of synthetic aperture radar automatic target recognition (SAR-ATR) is generally composed of the extraction of a set of features that transform the raw input into a representation, followed by a trainable classifier. The feature extractor is often hand designed with domain knowledge and can significantly impact the classification accuracy. By automatically learning hierarchies of features from massive training data, deep convolutional networks (ConvNets) recently have obtained state-of-the-art results in many computer vision and speech recognition tasks. However, when ConvNets was directly applied to SAR-ATR, it yielded severe overfitting due to limited training images. To reduce the number of free parameters, we present a new all-convolutional networks (A-ConvNets), which only consists of sparsely connected layers, without fully connected layers being used. Experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) benchmark data set illustrate that A-ConvNets can achieve an average accuracy of 99% on classification of ten-class targets and is significantly superior to the traditional ConvNets on the classification of target configuration and version variants.

Automatic Target Recognition in Synthetic Aperture Radar Imagery: A State-of-the-Art Review

2016-01-01

Khalid El-Darymli , Eric W. Gill , Peter McGuire +2 more

The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive … The purpose of this paper is to survey and assess the state-of-the-art in automatic target recognition for synthetic aperture radar imagery (SAR-ATR). The aim is not to develop an exhaustive survey of the voluminous literature, but rather to capture in one place the various approaches for implementing the SAR-ATR system. This paper is meant to be as self-contained as possible, and it approaches the SAR-ATR problem from a holistic end-to-end perspective. A brief overview for the breadth of the SAR-ATR challenges is conducted. This is couched in terms of a single-channel SAR, and it is extendable to multi-channel SAR systems. Stages pertinent to the basic SAR-ATR system structure are defined, and the motivations of the requirements and constraints on the system constituents are addressed. For each stage in the SAR-ATR processing chain, a taxonomization methodology for surveying the numerous methods published in the open literature is proposed. Carefully selected works from the literature are presented under the taxa proposed. Novel comparisons, discussions, and comments are pinpointed throughout this paper. A two-fold benchmarking scheme for evaluating existing SAR-ATR systems and motivating new system designs is proposed. The scheme is applied to the works surveyed in this paper. Finally, a discussion is presented in which various interrelated issues, such as standard operating conditions, extended operating conditions, and target-model design, are addressed. This paper is a contribution toward fulfilling an objective of end-to-end SAR-ATR system design.

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Hand Gesture Recognition Using Micro-Doppler Signatures With Convolutional Neural Network

2016-01-01

Youngwook Kim , Brian Toomajian

In this paper, we investigate the feasibility of recognizing human hand gestures using micro-Doppler signatures measured by Doppler radar with a deep convolutional neural network (DCNN). Hand gesture recognition using … In this paper, we investigate the feasibility of recognizing human hand gestures using micro-Doppler signatures measured by Doppler radar with a deep convolutional neural network (DCNN). Hand gesture recognition using radar can be applied to control electronic appliances. Compared with an optical recognition system, radar can work regardless of light conditions and it can be embedded in a case. We classify ten different hand gestures, with only micro-Doppler signatures on spectrograms without range information. The ten gestures, which included swiping from left to right, swiping from right to left, rotating clockwise, rotating counterclockwise, pushing, double pushing, holding, and double holding, were measured using Doppler radar and their spectrograms investigated. A DCNN was employed to classify the spectrograms, with 90% of the data utilized for training and the remaining 10% for validation. After five-fold validation, the classification accuracy of the proposed method was found to be 85.6%. With seven gestures, the accuracy increased to 93.1%.

Transfer Learning with Deep Convolutional Neural Network for SAR Target Classification with Limited Labeled Data

2017-08-31

Zhongling Huang , Zongxu Pan , Bin Lei

Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image … Tremendous progress has been made in object recognition with deep convolutional neural networks (CNNs), thanks to the availability of large-scale annotated dataset. With the ability of learning highly hierarchical image feature extractors, deep CNNs are also expected to solve the Synthetic Aperture Radar (SAR) target classification problems. However, the limited labeled SAR target data becomes a handicap to train a deep CNN. To solve this problem, we propose a transfer learning based method, making knowledge learned from sufficient unlabeled SAR scene images transferrable to labeled SAR target data. We design an assembled CNN architecture consisting of a classification pathway and a reconstruction pathway, together with a feedback bypass additionally. Instead of training a deep network with limited dataset from scratch, a large number of unlabeled SAR scene images are used to train the reconstruction pathway with stacked convolutional auto-encoders (SCAE) at first. Then, these pre-trained convolutional layers are reused to transfer knowledge to SAR target classification tasks, with feedback bypass introducing the reconstruction loss simultaneously. The experimental results demonstrate that transfer learning leads to a better performance in the case of scarce labeled training data and the additional feedback bypass with reconstruction loss helps to boost the capability of classification pathway.

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Radar-Based Human-Motion Recognition With Deep Learning: Promising Applications for Indoor Monitoring

2019-06-26

Sevgi Zübeyde Gürbüz , Moeness G. Amin

Deep learning (DL) has shown tremendous promise in radar applications that involve target classification and imaging. In the field of indoor monitoring, researchers have shown an interest in DL for … Deep learning (DL) has shown tremendous promise in radar applications that involve target classification and imaging. In the field of indoor monitoring, researchers have shown an interest in DL for classifying daily human activities, detecting falls, and monitoring gait abnormalities. Driving this interest are emerging applications related to smart and secure homes, assisted living, and medical diagnosis. The success of DL in providing an accurate real-time accounting of observed human-motion articulations fundamentally depends on the neural network structure, input data representation, and proper training. This article puts DL in the context of data-driven approaches for motion classification and compares its performance with other approaches employing handcrafted features. We discuss recent proposed enhancements of DL classification performance and report on important challenges and possible future research to realize its full potential.

Synthetic aperture radar

1991-01-01

Anthony Currie

A short introduction to the background and theory of synthetic aperture radar (SAR) imaging is given. Some of the key issues in SAR design are discussed and possible future developments … A short introduction to the background and theory of synthetic aperture radar (SAR) imaging is given. Some of the key issues in SAR design are discussed and possible future developments involving SAR operation with phased arrays are suggested.

Synthetic aperture radar signal processing

1989-12-01

M.R. Vant

Digital Beamforming on Receive: Techniques and Optimization Strategies for High-Resolution Wide-Swath SAR Imaging

2009-04-01

Nicolas Gebert , Gerhard Krieger , Alberto Moreira

Synthetic Aperture Radar (SAR) is a well-proven imaging technique for remote sensing of the Earth. However, conventional SAR systems are not capable of fulfilling the increasing demands for improved spatial … Synthetic Aperture Radar (SAR) is a well-proven imaging technique for remote sensing of the Earth. However, conventional SAR systems are not capable of fulfilling the increasing demands for improved spatial resolution and wider swath coverage. To overcome these inherent limitations, several innovative techniques have been suggested which employ multiple receive-apertures to gather additional information along the synthetic aperture. These digital beamforming (DBF) on receive techniques are reviewed with particular emphasis on the multi-aperture signal processing in azimuth and a multi-aperture reconstruction algorithm is presented that allows for the unambiguous recovery of the Doppler spectrum. The impact of Doppler aliasing is investigated and an analytic expression for the residual azimuth ambiguities is derived. Further, the influence of the processing on the signal-to-noise ratio (SNR) is analyzed, resulting in a pulse repetition frequency (PRF) dependent factor describing the SNR scaling of the multi-aperture beamforming network. The focus is then turned to a complete high-resolution wide-swath SAR system design example which demonstrates the intricate connection between multi-aperture azimuth processing and the system architecture. In this regard, alternative processing approaches are compared with the multi-aperture reconstruction algorithm. In a next step, optimization strategies are discussed as pattern tapering, prebeamshaping-on-receive, and modified processing algorithms. In this context, the analytic expressions for both the residual ambiguities and the SNR scaling factor are generalized to cascaded beamforming networks. The suggested techniques can moreover be extended in many ways. Examples discussed are a combination with ScanSAR burst mode operation and the transfer to multistatic sparse array configurations.

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Introduction to Radar Systems

2021-12-03

Chengpeng Hao , Danilo Orlando , Jun Liu +1 more

Coded aperture imaging with uniformly redundant arrays

1978-02-01

E. E. Fenimore , T. M. Cannon

Uniformly redundant arrays (URA) have autocorrelation functions with perfectly flat sidelobes. The URA combines the high-transmission characteristics of the random array with the flat sidelobe advantage of the nonredundant pinhole … Uniformly redundant arrays (URA) have autocorrelation functions with perfectly flat sidelobes. The URA combines the high-transmission characteristics of the random array with the flat sidelobe advantage of the nonredundant pinhole arrays. This gives the URA the capability to image low-intensity, low-contrast sources. Furthermore, whereas the inherent noise in random array imaging puts a limit on the obtainable SNR, the URA has no such limit. Computer simulations show that the URA with significant shot and background noise is vastly superior to random array techniques without noise. Implementation permits a detector which is smaller than its random array counterpart.

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Edge-Optimized Lightweight YOLO for Real-Time SAR Object Detection

2025-06-24

Caiguang Zhang , Ruofeng Yu , Shuwen Wang +4 more | Remote Sensing

Synthetic Aperture Radar image object detection holds significant application value in both military and civilian domains. However, existing deep learning-based methods suffer from excessive model parameters and high computational costs, … Synthetic Aperture Radar image object detection holds significant application value in both military and civilian domains. However, existing deep learning-based methods suffer from excessive model parameters and high computational costs, making them impractical for real-time deployment on edge computing platforms. To address these challenges, this paper proposes a lightweight SAR object detection method optimized for edge devices. First, we design an efficient backbone network based on inverted residual blocks and the information bottleneck principle, achieving an optimal balance between feature extraction capability and computational resource consumption. Then, a Fast Feature Pyramid Network is constructed to enable efficient multi-scale feature fusion. Finally, we propose a decoupled network-in-network Head, which significantly reduces the computational overhead while maintaining detection accuracy. Experimental results demonstrate that the proposed method achieves comparable detection performance to state-of-the-art YOLO variants while drastically reducing computational complexity (4.4 GFLOP) and parameter count (1.9 M). On edge platforms (Jetson TX2 and Huawei Atlas DK 310), the model achieves real-time inference speeds of 34.2 FPS and 30.7 FPS, respectively, proving its suitability for resource-constrained, real-time SAR object detection scenarios.

RFiDAR: Contactless RFID and Radar Data Fusion for Enhanced Human Activity Recognition

2025-06-24

Muhammad Zakir Khan , William Taylor , Muhammad Usman +6 more | IEEE Internet of Things Magazine

Spaceborne Sparse SAR Imaging Mode Design: From Theory to Implementation

2025-06-22

Yufan Song , Hui Bi , Fuxuan Cai +3 more | Sensors

To satisfy the requirement of the modern spaceborne synthetic aperture radar (SAR) system, SAR imaging mode design makes a trade-off between resolution and swath coverage by controlling radar antenna sweeping. … To satisfy the requirement of the modern spaceborne synthetic aperture radar (SAR) system, SAR imaging mode design makes a trade-off between resolution and swath coverage by controlling radar antenna sweeping. Existing spaceborne SAR systems can perform earth observation missions well in various modes, but they still face challenges in data acquisition, storage, and transmission, especially for high-resolution wide-swath imaging. In the past few years, sparse signal processing technology has been introduced into SAR to try to solve these problems. In addition, sparse SAR imaging shows huge potential to improve system performance, such as offering wider swath coverage and higher recovered image quality. In this paper, the design scheme of spaceborne sparse SAR imaging modes is systematically introduced. In the mode design, we first design the beam positions of the sparse mode based on the corresponding traditional mode. Then, the essential parameters are calculated for system performance analysis based on radar equations. Finally, a sparse SAR imaging method based on mixed-norm regularization is introduced to obtain a high-quality image of the considered scene from the data collected by the designed sparse modes. Compared with the traditional mode, the designed sparse mode only requires us to obtain a wider swath coverage by reducing the pulse repetition rate (PRF), without changing the existing on-board system hardware. At the same time, the reduction in PRF can significantly reduce the system data rate. The problem of the azimuth ambiguity signal ratio (AASR) increasing from antenna beam scanning can be effectively solved by using the mixed-norm regularization-based sparse SAR imaging method.

An Efficient Sparse Synthetic Aperture Radar Imaging Method Based on L1-Norm and Total Variation Regularization

2025-06-20

Zhiqi Gao , Huiying Ma , Pingping Huang +3 more | Electronics

The continuous progress of synthetic aperture radar (SAR) imaging has led to a growing emphasis on the challenges involved in data acquisition and processing. And the challenges in data acquisition … The continuous progress of synthetic aperture radar (SAR) imaging has led to a growing emphasis on the challenges involved in data acquisition and processing. And the challenges in data acquisition and processing have become increasingly prominent. However, traditional SAR imaging models are limited by their large demand for data sampling and slow image reconstruction speeds, which is particularly prominent in large-scale scene applications. To overcome these limitations, this study proposes an innovative L1-Total Variation (TV) regularization sparse SAR imaging algorithm. The submitted algorithm constructs an imaging operator and an echo simulation operator to achieve decoupling in the azimuth and range dimensions, respectively, as well as to reduce the requirement for sampling data. In addition, a Newton acceleration iterative method is introduced to the optimization process, aiming to accelerate the speed of image reconstruction. Comparative analysis and experimental validation indicate that the proposed sparse SAR imaging algorithm outperforms conventional methods in resolution, target localization, and clutter suppression. The results suggest strong potential for rapid scene reconstruction and real-time monitoring in complex environments.

HRRPGraphNet++: Dynamic Graph Neural Network with Meta-Learning for Few-Shot HRRP Radar Target Recognition

2025-06-19

L. Q. Chen , Z. Pan , Qi Liu +1 more | Remote Sensing

High-Resolution Range Profile (HRRP) radar recognition suffers from data scarcity challenges in real-world applications. We present HRRPGraphNet++, a framework combining dynamic graph neural networks with meta-learning for few-shot HRRP recognition. … High-Resolution Range Profile (HRRP) radar recognition suffers from data scarcity challenges in real-world applications. We present HRRPGraphNet++, a framework combining dynamic graph neural networks with meta-learning for few-shot HRRP recognition. Our approach generates graph representations dynamically through multi-head self attention (MSA) mechanisms that adapt to target-specific scattering characteristics, integrated with a specialized meta-learning framework employing layer-wise learning rates. Experiments demonstrate state-of-the-art performance in 1-shot (82.3%), 5-shot (91.8%), and 20-shot (94.7%) settings, with enhanced noise robustness (68.7% accuracy at 0 dB SNR). Our hybrid graph mechanism combines physical priors with learned relationships, significantly outperforming conventional methods in challenging scenarios.

SAR 3D Reconstruction Based on Multi-Prior Collaboration

2025-06-19

Yangyang Wang , Zefeng Zhou , Zhiming He +6 more | Remote Sensing

Array synthetic aperture radar (SAR) three-dimensional (3D) image reconstruction enables the extraction of target distribution information in 3D space, supporting scattering characteristic analysis and structural interpretation. SAR image reconstruction remains … Array synthetic aperture radar (SAR) three-dimensional (3D) image reconstruction enables the extraction of target distribution information in 3D space, supporting scattering characteristic analysis and structural interpretation. SAR image reconstruction remains challenging due to issues such as noise contamination and incomplete echo data. By introducing sparse priors such as L1 regularization functions, image quality can be improved to a certain extent and the impact of noise can be reduced. However, in scenarios involving distributed targets, the aforementioned methods often fail to maintain continuous structural features such as edges and contours, thereby limiting their reconstruction performance and adaptability. Recent studies have introduced geometric regularization functions to preserve the structural continuity of targets, yet these lack multi-prior consensus, resulting in limited reconstruction quality and robustness in complex scenarios. To address the above issues, a novel array SAR 3D reconstruction method based on multi-prior collaboration (ASAR-MPC) is proposed in this article. In this method, firstly, each optimization module in 3D reconstruction based on multi-prior is treated as an independent function module, and these modules are reformulated as parallel operations rather than sequential utilization. During the reconstruction process, the solution is constrained within the solution space of the module, ensuring that the SAR image simultaneously satisfies multiple prior conditions and achieves a coordinated balance among different priors. Then, a collaborative equilibrium framework based on Mann iteration is presented to solve the optimization problem of 3D reconstruction, which can ensure convergence to an equilibrium point and achieve the joint optimization of all modules. Finally, a series of simulation and experimental tests are described to validate the proposed method. The experimental results show that under limited echo and noise conditions, the proposed method outperforms existing methods in reconstructing complex target structures.

Incremental SAR Automatic Target Recognition with Divergence-Constrained Class-Specific Dictionary Learning

2025-06-18

Xiaojie Ma , Xusong Bu , Dezhao Zhang +2 more | Remote Sensing

Synthetic aperture radar (SAR) automatic target recognition (ATR) plays a pivotal role in SAR image interpretation. While existing approaches predominantly rely on batch learning paradigms, their practical deployment is constrained … Synthetic aperture radar (SAR) automatic target recognition (ATR) plays a pivotal role in SAR image interpretation. While existing approaches predominantly rely on batch learning paradigms, their practical deployment is constrained by the sequential arrival of training data and high retraining costs. To overcome this challenge, this paper introduces a divergence-constrained incremental dictionary learning framework that enables progressive model updates without full data reprocessing. Specifically, firstly, this method learns class-specific dictionaries for each target category via sub-dictionary learning, where the learning process for a specific class does not involve data from other classes. Secondly, the intra-class divergence constraint is incorporated during sub-dictionary learning to address the challenges of significant intra-class variations and minor inter-class differences in SAR targets. Thirdly, the sparse representation coefficients of the target to be classified are solved across all sub-dictionaries, followed by the computation of corresponding reconstruction errors and intra-class divergence metrics to achieve classification. Finally, when the targets of new categories are obtained, the corresponding class-specific dictionaries are calculated and added to the learned dictionary set. In this way, the incremental update of the SAR ATR system is completed. Experimental results on the MSTAR dataset indicate that our method attains >96.62% accuracy across various incremental scenarios. Compared with other state-of-the-art methods, it demonstrates better recognition performance and robustness.

Dynamics-based realistic simulator for rotation rate estimation of quadcopter drones with multistatic radar

2025-06-17

Lorenzo Tasciotti , Matteo Scafoni , Andrea Quirini +1 more | International Journal of Microwave and Wireless Technologies

Abstract In this paper, we introduce and validate signal processing techniques for the estimation of the individual rotation rates of multicopter’s Unmanned Aerial Vehicle (UAV), by exploiting a multistatic radar … Abstract In this paper, we introduce and validate signal processing techniques for the estimation of the individual rotation rates of multicopter’s Unmanned Aerial Vehicle (UAV), by exploiting a multistatic radar echoes. To validate the techniques, which have been introduced in our previous works, in this paper, we present a simulator for the multistatic radar echoes scattered by a UAV that integrates quadcopter’s aerodynamics with electromagnetic modeling to generate realistic radar return, characterized by blades rotating in different directions and with different rates depending on the flight trajectory to be traveled. This simulator enables the validation of signal processing. We leverage the simulator to assess the effectiveness of autocorrelation and cross-correlation (XCF) techniques in separating multiple propellers, both in hovering and along a realistic flight trajectory. Simulated results confirm that XCF allows distinguishing counter-rotating propellers, while co-rotating ones remain unresolved due to their similar speeds. The simulator also demonstrates how variations in rotation rates can be used to infer the presence and weight of a payload. Experimental validation with a C-band continuous wave radar confirms the findings and highlights the impact of material properties on resolution. Finally, we exploit the simulator to investigate the effect of higher carrier frequencies, showing that increasing the operating frequency improves the ability to discriminate co-rotating propellers, supporting improved UAV classification, payload estimation, and trajectory prediction for anti-drone applications.

DS-YOLO: A SAR Ship Detection Model for Dense Small Targets

2025-06-16

Ying Shen , Qi Gao | Radioengineering

TIAR-SAR: An Oriented SAR Ship Detector Combining a Task Interaction Head Architecture with Composite Angle Regression

2025-06-13

Yu Gu , M.-C. Fang , Dongliang Peng | Remote Sensing

Oriented ship detection in Synthetic Aperture Radar (SAR) images has broad applications in maritime surveillance and other fields. While deep learning advancements have significantly improved ship detection performance, persistent challenges … Oriented ship detection in Synthetic Aperture Radar (SAR) images has broad applications in maritime surveillance and other fields. While deep learning advancements have significantly improved ship detection performance, persistent challenges remain for existing methods. These include the inherent misalignment between regression and classification tasks and the boundary discontinuity problem in oriented object detection. These issues hinder efficient and accurate ship detection in complex scenarios. To address these challenges, we propose TIAR-SAR, a novel oriented SAR ship detector featuring a task interaction head and composite angle regression. First, we propose a task interaction detection head (Tihead) capable of predicting both oriented bounding boxes (OBBs) and horizontal bounding boxes (HBBs) simultaneously. Within the Tihead, a “decompose-then-interact” structure is designed. This structure not only mitigates feature misalignment but also promotes feature interaction between regression and classification tasks, thereby enhancing prediction consistency. Second, we propose a joint angle refinement mechanism (JARM). The JARM addresses the non-differentiability problem of the traditional rotated Intersection over Union (IoU) loss through the design of a composite angle regression loss (CARL) function, which strategically combines direct and indirect angle regression methods. A boundary angle correction mechanism (BACM) is then designed to enhance angle estimation accuracy. During inference, BACM dynamically replaces an object’s OBB prediction with its corresponding HBB if the OBB exhibits excessive angle deviation when the angle of the object is near the predefined boundary. Finally, the performance and applicability of the proposed methods are evaluated through extensive experiments on multiple public datasets, including SRSDD, HRSID, and DOTAv1. Experimental results derived from the use of the SRSDD dataset demonstrate that the mAP50 of the proposed method reaches 63.91%, an improvement of 4.17% compared with baseline methods. The detector achieves 17.42 FPS on 1024 × 1024 images using an RTX 2080 Ti GPU, with a model size of only 21.92 MB. Comparative experiments with other state-of-the-art methods on the HRSID dataset demonstrate the proposed method’s superior detection performance in complex nearshore scenarios. Furthermore, when further tested on the DOTAv1 dataset, the mAP50 can reach 79.1%.

Robust SAR Waveform Design for Extended Target in Spectrally Dense Environments

2025-06-12

Rui Zhang , Fuwei Wu , Bing Gao +3 more | Sensors

To enhance the signature of an extended target in a SAR image, a robust waveform design method is presented for spectrally dense environments. First, the problem is formulated by maximizing … To enhance the signature of an extended target in a SAR image, a robust waveform design method is presented for spectrally dense environments. First, the problem is formulated by maximizing the worst-case signal-to-clutter ratio (SCR) over the uncertainty set of statistics for both target and background scattering characteristics, subject to energy, similarity, and spectrum constraints. Second, the closed-form solutions for the uncertain statistics are derived. The problem of maximizing worst-case SCR is boiled down to a nonconvex fractional quadratically constrained quadratic problem (QCQP). Resorting to the Dinkelbach’s algorithm and Lagrange duality, the QCQP is split into a series of solvable semidefinite programming problems. A convergence analysis is conducted, where a sufficient condition for global convergence is derived. Finally, numerical examples are presented to demonstrate the performance of the proposed scheme.

mmMulti: Multi-person Action Recognition Based on Multi-task Learning Using Millimeter Waves

2025-06-09

Rui Zhou , Songlin Li , Hongwang Zhang +2 more | Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies

Action recognition using millimeter waves (mmWave) has shown great potential in various fields. However, the state-of-the-art still falls short in terms of multi-person action recognition inclusive interactive action recognition. In … Action recognition using millimeter waves (mmWave) has shown great potential in various fields. However, the state-of-the-art still falls short in terms of multi-person action recognition inclusive interactive action recognition. In this paper, we propose mmMulti, a multi-person action recognition method based on multi-task learning using millimeter waves. To this end, we first segregate the mmWave data and assign them to each of multiple persons, and propose two new input data---compressed Doppler map (CDM) and point trajectory segments (PTS)---to represent the patterns and sequential characteristics of actions. Next, we leverage ConvNeXt to extract pattern features from CDM and leverage Transformer to extract sequential features from PTS, and fuse them by a cross-attention mechanism. Finally, we custom-design a multi-task learning model to recognize independent and interactive actions from multiple concurrent persons, enabling mmMulti to recognize single-person actions, multi-person independent actions and multi-person interactive actions. We implement mmMulti on a commercial mmWave radar and conduct extensive experiments. mmMulti achieves single-person action recognition accuracy of 99.64%, independent action recognition accuracy of 91.03% for two persons, 72.38% for three persons, 64.75% for four persons, and interactive action recognition accuracy of 100% for two persons. To the best of our knowledge, mmMulti is the first work in the field of mmWave sensing to differentiate both independent and interactive actions in multi-person scenarios, based on a multi-task learning model to accomplish multiple tasks simultaneously.

An improved keystone transform implementation and its application in an S-band LFMCW Doppler radar

2025-06-03

Liang Zhang , Qinglei Du , Weijian Liu +2 more | Digital Signal Processing

Anti-interference methods for narrowband and repeater-modulation interference in airborne SAR systems

2025-06-01

Kai Chen , Yongbo Zhao , Kai Jiang +3 more | Journal of Electronic Science and Technology

Online Sparse Reconstruction for Real Aperture Radar by Beam Recursive-Sliding Updating Framework

2025-05-29

Xichen Yin , Deqing Mao , Yongchao Zhang +4 more | Remote Sensing

Real aperture radar (RAR) can acquire the forward-looking target scene of interest continuously in scanning mode by arbitrary imaging geometry; however, the achievable angular resolution is predominantly governed by the … Real aperture radar (RAR) can acquire the forward-looking target scene of interest continuously in scanning mode by arbitrary imaging geometry; however, the achievable angular resolution is predominantly governed by the physical dimensions of the antenna’s aperture. In contemporary radar imaging methodologies, the reconstruction of sparsely distributed targets can be effectively formulated as an L1-regularized optimization framework through the exploitation of a priori sparsity constraints, thereby enabling the generation of enhanced-resolution forward-looking radar imagery. Nevertheless, traditional target reconstruction methods based on the sparse regularization framework are implemented after batch data collection, which comes at the cost of significant operational complexity and storage space. To address this challenge, an online sparse reconstruction method based on a beam recursive-sliding (BRS) updating framework is proposed to achieve fast target reconstruction. First, the antenna measurement matrix is repaired to reduce the imaging edge information error. Then, due to the independence of the echo data within two beamwidths, a beam recursive updating method is proposed for each two beamwidths echo data by the structural properties of the repaired antenna measurement matrix. Finally, based on the proposed beam recursive updating method, a sliding updating approach is proposed for the whole imaging region to reduce the computational redundancy and storage requirement. Simulation and experimental data demonstrate the effectiveness of the proposed BRS updating framework.

Research on the Design of Signal Processing Algorithm Scheme for Vehicle-mounted Millimeter-wave Radar

2025-05-28

H. Ye | Deleted Journal

Among the common vehicle-mounted radars, vehicle-mounted millimeter-wave radar is an important component of vehicle-mounted sensors. The core of the vehicle-mounted millimeter-wave radar system is its signal processing module. Signal processing … Among the common vehicle-mounted radars, vehicle-mounted millimeter-wave radar is an important component of vehicle-mounted sensors. The core of the vehicle-mounted millimeter-wave radar system is its signal processing module. Signal processing algorithms based on digital signal processing technology have extremely important and far-reaching significance for the research and development of vehicle-mounted millimeter-wave radar. This paper analyzes the overall structure and basic working principle of the vehicle-mounted millimeter-wave radar system, designs a signal processing algorithm structure scheme of "Beat - Beamforming - Fourier spectrum analysis -MTD", and studies three key algorithms involved in FMCW radar signal processing: CFAR detection, MTD technology, and MTD velocity pairing method.