TY - GEN
T1 - Multiple-measurement vector model and its application to through-the-wall radar imaging
AU - Yang, J.
AU - Bouzerdoum, A.
AU - Tivive, F. H.C.
AU - Amin, M. G.
PY - 2011
Y1 - 2011
N2 - 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 point-like 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 produces better TWR images in terms of image quality and detection accuracy.
AB - 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 point-like 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 produces better TWR images in terms of image quality and detection accuracy.
KW - Compressed Sensing
KW - Multiple Polarizations
KW - Multiple-Measurement Vectors
KW - Through-the-Wall Radar Imaging
UR - http://www.scopus.com/inward/record.url?scp=80051655932&partnerID=8YFLogxK
U2 - 10.1109/ICASSP.2011.5947035
DO - 10.1109/ICASSP.2011.5947035
M3 - Conference contribution
AN - SCOPUS:80051655932
SN - 9781457705397
T3 - ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
SP - 2672
EP - 2675
BT - 2011 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011 - Proceedings
T2 - 36th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2011
Y2 - 22 May 2011 through 27 May 2011
ER -