The Fundamental Trajectory Reconstruction Results of Ground Moving Target From Single-Channel CSAR Geometry

Wu, Wei; An, Daoxiang; Luo, Yanlin; Zhou, Zhimin

doi:10.1109/tgrs.2018.2823310

Cited by 33 publications

(17 citation statements)

References 37 publications

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“…In (8), by replacing R i with R pi given in ( 10), S i is now approximated as (11) whereK xi andK yi are the Cartesian wavenumber in ReCS, given byK…”

Section: B Wavenumber Distribution After Refocusingmentioning

confidence: 99%

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Orthorectified Polar Format Algorithm for Generalized Spotlight SAR Imaging With DEM

Shankar

Alaee-Kerahroodi

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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In conventional polar format algorithm (PFA), the effective imaging scene is bounded to a limited region near the reference point unless postprocessing is utilized. In a previous paper, refocusing and zoom-in PFA (RZPFA) for curvilinear spotlight SAR imaging were proposed to produce a refocused image for an arbitrary region of interest (ROI) with constant elevation. However, for certain applications, the residual distortion and defocus caused by rugged terrain could not be ignored. In this article, RZPFA is adapted to incorporate the known digital elevation model (DEM) into the imaging process, which is named as orthorectified PFA (OPFA). With just little additional computations than RZPFA, OPFA can realize georeferenced orthorectified imaging via a nonuniform fast Fourier transform of type 3 (NuFFT-3) without the need of postprocessing. The quantitative metrics for the residual DEM distortion and residual DEM defocus were also derived to determine the effective imaging extent of OPFA. Within the effective extent, the proposed OPFA can obtain an orthorectified image efficiently, and the image has a very high quality comparable to backprojection (BP). The imaging results of measured echo and DEM data demonstrated the effectiveness of the proposed algorithm. Index Terms-Digital elevation model (DEM), georeferenced imaging, nonuniform fast Fourier transform, orthorectified synthetic aperture radar (SAR) imaging, polar format algorithm (PFA). I. INTRODUCTIONT HE past decades have witnessed an ever-increasing development in the fields of remote sensing [1], among which synthetic aperture radar (SAR) is of great importance for its unique all-day, all-weather sensing capability [2]. Similar to many other fields, the research of SAR has also entered the era of image processing based on deep learning [3], and commercial SAR startups are springing up [4], [5]. The research in SAR imaging has focused on algorithms for novel modes, such as bistatic SAR [6], multistatic SAR [7], multiple input multiple output SAR (MIMO-SAR

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“…In (8), by replacing R i with R pi given in ( 10), S i is now approximated as (11) whereK xi andK yi are the Cartesian wavenumber in ReCS, given byK…”

Section: B Wavenumber Distribution After Refocusingmentioning

confidence: 99%

“…SAR [9], [10], as well as imaging of moving targets [11] and computational imaging algorithms with constraints [12].…”

mentioning

confidence: 99%

Orthorectified Polar Format Algorithm for Generalized Spotlight SAR Imaging With DEM

Shankar

Alaee-Kerahroodi

et al. 2021

IEEE Trans. Geosci. Remote Sensing

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show abstract

“…where S weight = sum(ROI(k)) is the weighted area of and S unweight = numel(ROI(k)) is its unweighted area. It is easy to obtain the range of values of T ROI , as in [1,2]. In general, T ROI is set between 1.3 and 1.5 for satisfactory detection probability of moving target at all speeds, while suppressing false alarms as much as possible.…”

Section: Fusion and Threshold Determinationmentioning

confidence: 99%

“…The synthetic aperture radar (SAR) combines pulse compression and the synthetic aperture to obtain high resolution images along the directions of the range and the azimuth. It is regarded as an important means of earth observations and battlefield surveillance [1][2][3][4]. A military target may not stay motionless but seek to attack while constantly moving to improve its probability of survival.…”

Section: Introductionmentioning

confidence: 99%

Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

Chen

et al. 2021

Remote Sensing

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Video synthetic aperture radar (VideoSAR) can detect and identify a moving target based on its shadow. A slowly moving target has a shadow with distinct features, but it cannot be detected by state-of-the-art difference-based algorithms because of minor variations between adjacent frames. Furthermore, the detection boxes generated by difference-based algorithms often contain such defects as misalignments and fracture. In light of these problems, this study proposed a robust moving target detection (MTD) algorithm for objects on the ground by fusing the background frame detection results and the difference between frames over multiple intervals. We also discuss defects that occur in conventional MTD algorithms. The difference in background frame was introduced to overcome the shortcomings of difference-based algorithms and acquire the shadow regions of objects. This was fused with the multi-interval frame difference to simultaneously extract the moving target at different velocities while identifying false alarms. The results of experiments on empirically acquired VideoSAR data verified the performance of the proposed algorithm in terms of detecting a moving target on the ground based on its shadow.

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“…Video SAR (ViSAR) can display sequential SAR images at a certain rate and has the ability of a dynamic monitoring scene. Compared with traditional SAR imaging results (i.e., static images), ViSAR can provide long-time persistent surveillance of ROI at a high frame rate and can obtain the trajectories of ground moving targets [1,2]. In order to better monitor the scene, a high frame rate and high resolution are required.…”

Section: Introductionmentioning

confidence: 99%

A Novel Generation Method of High Quality Video Image for High Resolution Airborne ViSAR

Chen

et al. 2021

Remote Sensing

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Video synthetic aperture radar (ViSAR) can provide long-time surveillance of a region of interest (ROI), which is one of the hotspot directions in the SAR field. In order to better display ViSAR, a high resolution and high frame rate are needed. Azimuth integration angle and sub-aperture overlapping ratio, which determine the image resolution and frame rate, respectively, are analyzed in depth in this paper. For SAR imaging algorithm, polar format algorithm (PFA) is applied, which not only has high efficiency but is also easier to integrate with autofocus algorithms. Due to sensitivity to motion error, it is very difficult to obtain satisfactory focus quality, especially for SAR systems with a high carrier frequency. The three-step motion compensation (MOCO) proposed in this paper, which combines GPS-based MOCO, map-drift (MD) and phase gradient autofocus (PGA), can effectively compensate for motion error, especially for short wavelengths. In ViSAR, problems such as jitter, non-uniform grey scale and low image signal noise ratio (SNR) between different aspects images also need to be considered, so a ViSAR generation method is proposed to solve the above problems. Finally, the results of ViSAR in THz and Ku band demonstrate the effectiveness and practicability of the proposed method.

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The Fundamental Trajectory Reconstruction Results of Ground Moving Target From Single-Channel CSAR Geometry

Cited by 33 publications

References 37 publications

Orthorectified Polar Format Algorithm for Generalized Spotlight SAR Imaging With DEM

Orthorectified Polar Format Algorithm for Generalized Spotlight SAR Imaging With DEM

Detecting Moving Target on Ground Based on Its Shadow by Using VideoSAR

A Novel Generation Method of High Quality Video Image for High Resolution Airborne ViSAR

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