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

• Main Authors: Donatien Sabushimike, Seung You Na, Jin Young Kim, Ngoc Nam Bui, Kyung Sik Seo, Gil Gyeom Kim
• Format: Article
• Language: English
• Published: MDPI AG 2016-09-01
• Series: Sensors
• Subjects: UWB; moving target detection; background subtraction; matrix decomposition; low-rank; sparse; RPCA; augmented Lagrange multiplier; online processing; real-time processing
• Online Access: http://www.mdpi.com/1424-8220/16/9/1409

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.