Estimating the velocity of moving targets in multichannel SAR images by fourier analysis and optimally-weighted velocity averaging

2021 ◽  
Vol 13 (3) ◽  
pp. 226-235
Author(s):  
Yahua Ren ◽  
Junfeng Wang
Keyword(s):  
Fourier Analysis ◽  
Moving Targets ◽  
Sar Images

scholarly journals Extended GLRT Detection of Moving Targets for Multichannel SAR Based on Generalized Steering Vector

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1478
Author(s):  
Chong Song ◽  
Bingnan Wang ◽  
Maosheng Xiang ◽  
Wei Li
Keyword(s):  
Likelihood Ratio ◽  
Likelihood Ratio Test ◽  
Moving Target ◽  
Ratio Test ◽  
Constant False Alarm Rate ◽  
Moving Targets ◽  
Sar Images ◽  
Steering Vector ◽  
Current Cell ◽  
Data Vector

A generalized likelihood ratio test (GLRT) with the constant false alarm rate (CFAR) property was recently developed for adaptive detection of moving targets in focusing synthetic aperture radar (SAR) images. However, in the multichannel SAR-ground moving-target indication (SAR-GMTI) system, image defocus is inevitable, which will remarkably degrade the performance of the GLRT detector, especially for the lower radar cross-section (RCS) and slower radial velocity moving targets. To address this issue, based on the generalized steering vector (GSV), an extended GLRT detector is proposed and its performance is evaluated by the optimum likelihood ratio test (LRT) in the Neyman-Pearson (NP) criterion. The joint data vector formulated by the current cell and its adjacent cells is used to obtain the GSV, and then the extended GLRT is derived, which coherently integrates signal and accomplishes moving-target detection and parameter estimation. Theoretical analysis and simulated SAR data demonstrate the effectiveness and robustness of the proposed detector in the defocusing SAR images.

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

2019 ◽  
Vol 13 (8) ◽  
pp. 1279-1286 ◽  
Author(s):  
Yichang Chen ◽  
Gang Li ◽  
Qun Zhang
Keyword(s):  
Minimum Entropy ◽  
Moving Targets ◽  
Sar Images

Refocussing of moving targets in SAR images based on inversion mapping and ISAR processing

2011 ◽  
Author(s):  
M. Martorella ◽  
F. Berizzi ◽  
E. Giusti ◽  
A. Bacci
Keyword(s):  
Moving Targets ◽  
Sar Images

Robust moving targets detection and velocity estimation using multi-channel and multi-look SAR images

2010 ◽  
Vol 90 (6) ◽  
pp. 2009-2019 ◽  
Author(s):  
Shengqi Zhu ◽  
Guisheng Liao ◽  
Zhengguang Zhou ◽  
Yi Qu
Keyword(s):  
Velocity Estimation ◽  
Moving Targets ◽  
Sar Images

scholarly journals Refocusing of Moving Targets in SAR Images via Parametric Sparse Representation

2017 ◽  
Vol 9 (8) ◽  
pp. 795 ◽  
Author(s):  
Yichang Chen ◽  
Gang Li ◽  
Qun Zhang ◽  
Jinping Sun
Keyword(s):  
Sparse Representation ◽  
Moving Targets ◽  
Sar Images

Efficient Detection and Imaging of Moving Targets in SAR Images Based on Chirp Scaling

2013 ◽  
Vol 51 (4) ◽  
pp. 2403-2416 ◽  
Author(s):  
Diego Cristallini ◽  
Debora Pastina ◽  
Fabiola Colone ◽  
Pierfrancesco Lombardo
Keyword(s):  
Moving Targets ◽  
Sar Images ◽  
Efficient Detection

scholarly journals Accurate detection method of moving target based on sequential SAR images with multiple azimuth squint angles

2021 ◽  
Vol 2083 (3) ◽  
pp. 032051
Author(s):  
Shiqi Yang ◽  
Yang Liu ◽  
Peili Xi ◽  
Chunsheng Li ◽  
Wei Yang ◽  
...  
Keyword(s):  
False Alarm ◽  
Detection Method ◽  
Synthetic Aperture ◽  
Moving Target ◽  
Constant False Alarm Rate ◽  
Moving Targets ◽  
Sar Images ◽  
Block Level ◽  
Detection Block ◽  
Motion Characteristics

Abstract In this paper, a novel moving target detection method for sequential Synthetic Aperture Radar (SAR) images with different azimuth-squint angles is proposed. In sequential SAR images, due to the movement of the target, the imaging position of moving targets among different frames differs. The method proposed in this paper uses this kind of motion characteristics to achieve the detection of moving targets in multi-frame SAR images. This algorithm can be divided into two parts: block-level detection and pixel-level detection. Block-level detection is achieved by stacked denoising autoencoders to extract the high-dimensional features of the moving target. Pixel-level detection consists of Local Binary Similarity Patterns (LBSP) coding as well as grayscale background subtraction. Pixel-level detection only needs to consider the pixels of foreground image pieces which contain moving targets. This method can not only increase the detection speed, but also suppress the false alarm problem caused by clutter. Experiments are carried out for verifying the validation of the method and the comparison are made between the proposed method and the traditional Constant False Alarm Rate (CFAR) algorithm.

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