Low-Rank Matrix Recovery Approach for Clutter Rejection in Real-Time IR-UWB Radar-Based Moving Target DetectionReport as inadecuate


Low-Rank Matrix Recovery Approach for Clutter Rejection in Real-Time IR-UWB Radar-Based Moving Target Detection


Low-Rank Matrix Recovery Approach for Clutter Rejection in Real-Time IR-UWB Radar-Based Moving Target Detection - Download this document for free, or read online. Document in PDF available to download.

1

Department of Electronics and Computer Engineering, Chonnam National University, 77 Yongbong-ro, Buk-gu, Gwangju 61186, Korea

2

MOMED Solution, Gwangju 61008, Korea





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Academic Editor: Changzhi Li

Abstract The detection of a moving target using an IR-UWB Radar involves the core task of separating the waves reflected by the static background and by the moving target. This paper investigates the capacity of the low-rank and sparse matrix decomposition approach to separate the background and the foreground in the trend of UWB Radar-based moving target detection. Robust PCA models are criticized for being batched-data-oriented, which makes them inconvenient in realistic environments where frames need to be processed as they are recorded in real time. In this paper, a novel method based on overlapping-windows processing is proposed to cope with online processing. The method consists of processing a small batch of frames which will be continually updated without changing its size as new frames are captured. We prove that RPCA via its Inexact Augmented Lagrange Multiplier IALM model can successfully separate the two subspaces, which enhances the accuracy of target detection. The overlapping-windows processing method converges on the optimal solution with its batch counterpart i.e., processing batched data with RPCA, and both methods prove the robustness and efficiency of the RPCA over the classic PCA and the commonly used exponential averaging method. View Full-Text

Keywords: UWB; moving target detection; background subtraction; matrix decomposition; low-rank; sparse; RPCA; augmented Lagrange multiplier; online processing; real-time processing UWB; moving target detection; background subtraction; matrix decomposition; low-rank; sparse; RPCA; augmented Lagrange multiplier; online processing; real-time processing





Author: Donatien Sabushimike 1, Seung You Na 1,* , Jin Young Kim 1, Ngoc Nam Bui 2, Kyung Sik Seo 2 and Gil Gyeom Kim 2

Source: http://mdpi.com/



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