Parametric Sparse Representation Method for Motion Parameter Estimation of Ground Moving Target

Gu, Fengshou; Zhang, Qun; Chen, Yi‐Chang; Huo, Wenjun; Ni, Jiacheng

doi:10.1109/jsen.2016.2604046

Cited by 11 publications

(7 citation statements)

References 21 publications

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“…Although the moving target of interest usually has strong scattering characteristics, the signal‐to‐clutter ratio in the original data domain is still relatively low. Some clutter suppression technologies, such as displaced phase centre antenna (DPCA) and along‐track interferometry (ATI), are proposed in [7, 8]. As for DPCA and ATI, multiple receiving channels are required.…”

Section: Introductionmentioning

confidence: 99%

Iterative minimum entropy algorithm for refocusing of moving targets in SAR images

Chen¹,

Zhang

2019

IET Radar, Sonar & Navigation

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Section: Introductionmentioning

confidence: 99%

Iterative minimum entropy algorithm for refocusing of moving targets in SAR images

Chen¹,

Zhang

2019

IET Radar, Sonar & Navigation

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“…These cheap aircraft mainly fly on the ground and the illegal manipulation on these aircraft can result in serious security issues. However, there are few literatures focusing on the slow moving target detection in the ground clutter [13][14][15][16][17]. A space-time adaptive processing method was proposed to detect the ground moving target with range migration (RM) [14].…”

Section: Introductionmentioning

confidence: 99%

“…A space-time adaptive processing method was proposed to detect the ground moving target with range migration (RM) [14]. In order to reduce the amount of echo data and achieve a wide observation swath, a parametric sparse representation method was used for the motion parameter estimation of the ground target in [15]. In particular, some spectrum zoom algorithms provide a new path for the LSS target detection.…”

Section: Introductionmentioning

confidence: 99%

Low-Altitude and Slow-Speed Small Target Detection Based on Spectrum Zoom Processing

Zhang

Sun

Shangguan

2018

Mathematical Problems in Engineering

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This paper proposes a spectrum zoom processing based target detection algorithm for detecting the weak echo of low-altitude and slow-speed small (LSS) targets in heavy ground clutter environments, which can be used to retrofit the existing radar systems. With the existing range-Doppler frequency images, the proposed method firstly concatenates the data from the same Doppler frequency slot of different images and then applies the spectrum zoom processing. After performing the clutter suppression, the target detection can be finally implemented. Through the theoretical analysis and real data verification, it is shown that the proposed algorithm can obtain a preferable spectrum zoom result and improve the signal-to-clutter ratio (SCR) with a very low computational load.

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“…(1) Methods based on the raw radar data [8][9][10][11][12]. These methods deal with the entire echo data reflected from both stationary background and moving targets.…”

Section: Introductionmentioning

confidence: 99%

“…The parametric sparse representation technique has been utilized to ISAR imaging of rotating targets [26][27][28], SAR motion compensation [29], and moving target motion parameters estimation [12]. Differing from the previous work where the process flow starts from entire raw data, the proposed method in this paper is based on ROI data.…”

Section: Introductionmentioning

confidence: 99%

Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

Chen

Zhang

et al. 2017

Remote Sensing

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Abstract:In this paper, a parametric sparse representation (PSR) method is proposed for refocusing of moving targets in synthetic aperture radar (SAR) images. In regular SAR images, moving targets are defocused due to unknown motion parameters. Refocusing of moving targets requires accurate phase compensation of echo data. In the proposed method, the region of interest (ROI) data containing the moving targets are extracted from the complex SAR image and represented in a sparse fashion through a parametric transform, which is related to the phase compensation parameter. By updating the reflectivities of moving target scatterers and the parametric transform in an iterative fashion, the phase compensation parameter can be accurately estimated and the SAR images of moving targets can be refocused well. The proposed method directly operates on small-size defocused ROI data, which helps to reduce the computational burden and suppress the clutter. Compared to other existing ROI-based methods, the proposed method can suppress asymmetric side-lobes and improve the image quality. Both simulated data and real SAR data collected by GF-3 satellite are used to validate the effectiveness of the proposed method.

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Parametric Sparse Representation Method for Motion Parameter Estimation of Ground Moving Target

Cited by 11 publications

References 21 publications

Iterative minimum entropy algorithm for refocusing of moving targets in SAR images

Iterative minimum entropy algorithm for refocusing of moving targets in SAR images

Low-Altitude and Slow-Speed Small Target Detection Based on Spectrum Zoom Processing

Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

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