Online subspace and sparse filtering for target tracking in reverberant environment

Li, Weichang; Subrahmanya, Niranjan; Xu, Feng

doi:10.1109/sam.2012.6250502

Cited by 4 publications

(3 citation statements)

References 12 publications

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“…We evaluated the detection performance of our method on the detection gain and the computation time. Our method was compared with the sequential random projection based subspace tracking and sparse filtering (SRPSS) method and fast data projection based subspace tracking and sparse filtering (FDPSS) method proposed in [ 21 ] and the accelerated proximal gradient (APG) method proposed in [ 22 ]. The three sets of underwater acoustic data were used in the comparison.…”

Section: Resultsmentioning

confidence: 99%

“…Low-rank and sparse decomposition can be achieved by different methods. Weichang Li et al and Feng-Xiang Ge et al achieved this decomposition by using the random projection algorithm and convex optimization, respectively [ 21 , 22 ]. In this section, we use DMD to achieve low-rank and sparse decomposition and apply it for moving target detection.…”

Section: Low-rank and Sparse Decomposition For Detectionmentioning

confidence: 99%

“…Combining multiframe data, the moving target echo can be separated from the reverberation background, which takes advantage of the potential low-rank structure of reverberation. Based on this idea, Weichang Li et al used a random projection algorithm for target tracking in the reverberant environment [ 21 ]. As an extension of their work, Feng-Xiang Ge et al used convex optimization to decompose the data matrix instead of random projection and obtained better results [ 22 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Moving Target Detection Using Dynamic Mode Decomposition

Yin

Liu

Zhu

et al. 2018

Sensors

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It is challenging to detect a moving target in the reverberant environment for a long time. In recent years, a kind of method based on low-rank and sparse theory was developed to study this problem. The multiframe data containing the target echo and reverberation are arranged in a matrix, and then, the detection is achieved by low-rank and sparse decomposition of the data matrix. In this paper, we introduce a new method for the matrix decomposition using dynamic mode decomposition (DMD). DMD is usually used to calculate eigenmodes of an approximate linear model. We divided the eigenmodes into two categories to realize low-rank and sparse decomposition such that we detected the target from the sparse component. Compared with the previous methods based on low-rank and sparse theory, our method improves the computation speed by approximately 4–90-times at the expense of a slight loss of detection gain. The efficient method has a big advantage for real-time processing. This method can spare time for other stages of processing to improve the detection performance. We have validated the method with three sets of underwater acoustic data.

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Section: Resultsmentioning

confidence: 99%

Section: Low-rank and Sparse Decomposition For Detectionmentioning

confidence: 99%