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

• Published in: Sensors (Basel, Switzerland) Vol. 16; no. 9; p. 1409
• Main Authors: Sabushimike, Donatien, Na, Seung You, Kim, Jin Young, Bui, Ngoc Nam, Seo, Kyung Sik, Kim, Gil Gyeom
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 01.09.2016; MDPI
• Subjects: augmented Lagrange multiplier; background subtraction; Clutter; Frames; Lagrange multiplier; Lagrange multipliers; low-rank; matrix decomposition; moving target detection; Moving targets; online processing; Radar detection; Real time; real-time processing; Rejection; RPCA; sparse; Sparse matrices; Subspaces; Target detection; Ultrawideband; Ultrawideband radar; UWB; online processing; UWB; real-time processing; sparse; low-rank; RPCA; matrix decomposition; background subtraction; moving target detection; augmented Lagrange multiplier
• ISSN: 1424-8220; 1424-8220
• DOI: 10.3390/s16091409

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.