A ViSAR-GMTI algorithm based on low-rank sparse decomposition and target trajectory region extraction

doi:10.7523/j.ucas.2022.082

Abstract

Abstract: Different observation angles cause changes in the backscattering coefficients of objects resulting in dynamic backgrounds in video synthetic aperture radar (ViSAR)，which is not conducive to the detection of moving objects in complex scenes. A ViSAR moving target detection method based on low-rank sparse decomposition and motion trajectory region extraction is proposed. First, considering the spatial continuity of the target and many interference factors in complex scenes, the conventional RPCA model is improved, and the structured sparsity-inducing norm and robust structure for dynamic background are applied in the model to obtain a better decomposition effect. Secondly, the setting of the local adaptive threshold is optimized, and the composite segmentation method is used to extract the motion trajectory area to further eliminate the interference. The mean background modeling method is used to complete the moving object detection in the trajectory area of the foreground image. Finally, the experimental results based on Qilu-1 data show the effectiveness of the proposed method, and the detection performance of the method is verified by comparative experiments.

Key words: video SAR (ViSAR), moving target detection, low-rank sparse decomposition, threshold segmentation

CLC Number:

TN957.52

YIN Zhongzheng, REN Yuwei, ZHENG Mingjie. A ViSAR-GMTI algorithm based on low-rank sparse decomposition and target trajectory region extraction[J]. Journal of University of Chinese Academy of Sciences, 2024, 41(4): 533-540.

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