Multiple-Measurement Vector model and its application to Through-the-Wall Radar Imaging

This paper addresses the problem of Through-the-Wall Radar Imaging (TWRI) using the Multiple-Measurement Vector (MMV) compressive sensing model. TWR image formation is reformulated as a compressed sensing (CS) problem, seeking a sparse representation in the spatial domain. In traditional CS-based through-the-wall radar imaging (TWRI) methods, the measurement matrix is vectorized so that a single measurement vector (SMV) model is applied to generate a sparse solution, which represents a scene comprising pointlike targets. For multiple measurement TWRI problems, the SMV model may produce a sub-optimum sparse solution. On the other hand, the proposed MMV model for TWRI generates a more sparse scene by processing all the measurements simultaneously. To evaluate the effectiveness of the proposed method, it is applied to fuse multiple polarization data to form the radar image. Based on simulated data with different number of measurements and noise levels, the proposed MMV-based TWRI method ...

• Type: Journal
• Year: 2011
• Where: ICASSP
• Authors: Jie Yang, Abdesselam Bouzerdoum, Fok Hing Chi Tivive, Moeness G. Amin
• Keywords: ICASSP 2011, Measurements, Signal Processing, Sparse, Sparse Solution