Ground Moving Target 2-D Velocity Estimation and Refocusing for Multichannel Maneuvering SAR with Fixed Acceleration

Tang, Xinxin; Tian, Bokun

doi:10.3390/s19173695

Cited by 9 publications

(6 citation statements)

References 39 publications

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“…where the first term mainly causes distinct offset to moving target in SAR image and the second term leads to defocusing of moving targets. A lot of literature has quantitatively analyzed the offset and defocusing of moving targets [39][40][41][42][43].…”

Section: Imaging Analysismentioning

confidence: 99%

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Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

et al. 2020

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Stable and efficient ground moving target tracking and refocusing is a hard task in synthetic aperture radar (SAR) data processing. Since shadows in video-SAR indicate the actual positions of moving targets at different moments without any displacement, shadow-based methods provide a new approach for ground moving target processing. This paper constructs a novel framework to refocus ground moving targets by using shadows in video-SAR. To this end, an automatic-registered SAR video is first obtained using the video-SAR back-projection (v-BP) algorithm. The shadows of multiple moving targets are then tracked using a learning-based tracker, and the moving targets are ultimately refocused via a proposed moving target back-projection (m-BP) algorithm. With this framework, we can perform detecting, tracking, imaging for multiple moving targets integratedly, which significantly improves the ability of moving-target surveillance for SAR systems. Furthermore, a detailed explanation of the shadow of a moving target is presented herein. We find that the shadow of ground moving targets is affected by a target’s size, radar pitch angle, carrier frequency, synthetic aperture time, etc. With an elaborate system design, we can obtain a clear shadow of moving targets even in X or C band. By numerical experiments, we find that a deep network, such as SiamFc, can easily track shadows and precisely estimate the trajectories that meet the accuracy requirement of the trajectories for m-BP.

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Section: Imaging Analysismentioning

confidence: 99%

“…According to the analysis in Section II, the exist of ∆s(n) in ( 9) caused by ∆R(n) leads to offset and defocusing of the moving target in the SAR image [39][40][41][42][43], and the reference signal in (8) needs to be modified as the form of Equation ( 5) to refocus the moving target. Thus, we have…”

Section: Moving Target Back-projectionmentioning

confidence: 99%

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

et al. 2020

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“…Synthetic aperture radar (SAR) is a radar imaging technology and has been applied in different fields such as ocean surface monitoring [1], target identification and classification [2,3], resource exploration [4], and natural calamity monitoring [5] successfully because of its all-day and all-weather working capabilities. Traditional SAR images only reflect the two-dimensional (2D) information of targets while usually lose targets' information in the height direction; they cannot reflect the three-dimensional (3D) structure of targets.…”

Section: Introductionmentioning

confidence: 99%

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

Tian

et al. 2021

Remote Sensing

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Because of the three-dimensional (3D) imaging scene’s sparsity, compressed sensing (CS) algorithms can be used for linear array synthetic aperture radar (LASAR) 3D sparse imaging. CS algorithms usually achieve high-quality sparse imaging at the expense of computational efficiency. To solve this problem, a fast Bayesian compressed sensing algorithm via relevance vector machine (FBCS–RVM) is proposed in this paper. The proposed method calculates the maximum marginal likelihood function under the framework of the RVM to obtain the optimal hyper-parameters; the scattering units corresponding to the non-zero optimal hyper-parameters are extracted as the target-areas in the imaging scene. Then, based on the target-areas, we simplify the measurement matrix and conduct sparse imaging. In addition, under low signal to noise ratio (SNR), low sampling rate, or high sparsity, the target-areas cannot always be extracted accurately, which probably contain several elements whose scattering coefficients are too small and closer to 0 compared to other elements. Those elements probably make the diagonal matrix singular and irreversible; the scattering coefficients cannot be estimated correctly. To solve this problem, the inverse matrix of the singular matrix is replaced with the generalized inverse matrix obtained by the truncated singular value decomposition (TSVD) algorithm to estimate the scattering coefficients correctly. Based on the rank of the singular matrix, those elements with small scattering coefficients are extracted and eliminated to obtain more accurate target-areas. Both simulation and experimental results show that the proposed method can improve the computational efficiency and imaging quality of LASAR 3D imaging compared with the state-of-the-art CS-based methods.

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“…The acceleration and third-order phase were considered in [15,16]. Tang et al [17] achieved 2D velocity estimation of moving targets and refocusing based on back projection and velocity SAR (another multichannel SAR-GMTI technique). Nevertheless, these methods introduce nonlinear operations, which degrade the performance in the case of low SNR.…”

Section: Introductionmentioning

confidence: 99%

A Hybrid SAR/ISAR Approach for Refocusing Maritime Moving Targets with the GF-3 SAR Satellite

Yan

Zhang

2020

Sensors

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Due to self-motion and sea waves, moving ships are typically defocused in synthetic aperture radar (SAR) images. To focus non-cooperative targets, the inverse SAR (ISAR) technique is commonly used with motion compensation. The hybrid SAR/ISAR approach allows a long coherent processing interval (CPI), in which SAR targets are processed with ISAR processing, and exploits the advantages of both SAR and ISAR to generate well-focused images of moving targets. In this paper, based on hybrid SAR/ISAR processing, we propose an improved rank-one phase estimation method (IROPE). By using an iterative two-step convergence approach in the IROPE, the proposed method achieves accurate phase error, maintains robustness to noise and performs well in estimating various phase errors. The performance of the proposed method is analyzed by comparing it with other focusing algorithms in terms of processing simulated data and real complex image data acquired by Gaofen-3 (GF-3) in spotlight mode. The results demonstrate the effectiveness of the proposed method.

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Ground Moving Target 2-D Velocity Estimation and Refocusing for Multichannel Maneuvering SAR with Fixed Acceleration

Cited by 9 publications

References 39 publications

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

Ground Moving Target Tracking and Refocusing Using Shadow in Video-SAR

Fast Bayesian Compressed Sensing Algorithm via Relevance Vector Machine for LASAR 3D Imaging

A Hybrid SAR/ISAR Approach for Refocusing Maritime Moving Targets with the GF-3 SAR Satellite

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