A Novel Multi-Scan Joint Method for Slow-Moving Target Detection in the Strong Clutter via RPCA

Su, Jia; Cui, G.; Li, Tao; Fan, Yifei; Tao, Mingliang; Wang, Haitao; Zhang, Xiang

doi:10.1109/igarss47720.2021.9554191

Cited by 2 publications

(3 citation statements)

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“…The robust principal component analysis (RPCA) technique focuses on a matrix's decomposition into a low-rank and sparse matrix. In recent years, RPCA has been used in ground penetrating radar (GPR) and synthetic aperture radar (SAR) applications to remove background noise [18], [19], [22]. A B-Scan or range profile image is composed of several A-Scan into a twodimensional matrix.…”

Section: Robust Principal Component Analysismentioning

confidence: 99%

“…where 𝑳, 𝑺, and 𝑵 denote a low-rank matrix of clutter, a sparse matrix of moving targets, and a noise matrix, respectively. Matrix 𝑫 can be decomposed into a sparse matrix and a low-rank matrix using Go Decomposition (GoDec) algorithm, as presented by [18], [23]. where ‖.…”

Section: Robust Principal Component Analysismentioning

confidence: 99%

“…A moving target indication (MTI) filter introduce to detect a slow-moving target [17]. Slowmoving target detection under a cluttered environment using robust principal component analysis (RPCA) is presented in [18], [19]. RPCA outperforms MTI due to RPCA can suppress clutter effectively and possibly prevent moving targets.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Development of FMCW Radar Signal Processing for High-Speed Railway Collision Avoidance

Putri¹,

Kurniawan²,

Hasanah³

et al. 2022

J. Elektron. dan Telekomun.

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Collision is the main issue in safe transportation, including in the railway system. Sensor systems have been developed to detect obstacles to prevent a collision, such as using cameras. One disadvantage of the camera systems is that performance detection decreases in a not clean environment, like the target position behind the fogs. This paper discusses the development of frequency modulated continuous wave (FMCW) radar signal processing for high-speed railway collision avoidance. The development of radar signal processing combines a two-dimensional constant false alarm rate (2D-CFAR) and robust principal component analysis (RPCA) to detect moving targets under clutter. Cell average (CA) and Greatest of CA (GOCA) CFAR are evaluated under a cluttered wall environment along the railway track. From the experiment, the development of FMCW radar can detect stationary or moving obstacles around 675 meters in front of the locomotive. Combining 2D-CFAR and RPCA algorithm outperforms average background subtraction in extracting moving targets from strong clutter signals along the railway track.

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Section: Robust Principal Component Analysismentioning

confidence: 99%

Section: Robust Principal Component Analysismentioning

confidence: 99%