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Content Provider | IEEE Xplore Digital Library |
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Author | Yang, J. Bouzerdoum, A. Tivive, F. H. C. Amin, M. G. |
Copyright Year | 2011 |
Description | Author affiliation: School of Electrical, Computer and Telecommunications Engineering, University of Wollongong, NSW 2522, Australia (Yang, J.; Bouzerdoum, A.; Tivive, F. H. C.) || Centre for Advanced Communications, Villanova University, PA 19085, USA (Amin, M. G.) |
Abstract | 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. |
Starting Page | 2672 |
Ending Page | 2675 |
File Size | 362133 |
Page Count | 4 |
File Format | |
ISBN | 9781457705380 |
ISSN | 15206149 |
e-ISBN | 9781457705397 |
e-ISBN | 9781457705373 |
DOI | 10.1109/ICASSP.2011.5947035 |
Language | English |
Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Publisher Date | 2011-05-22 |
Publisher Place | Czech Republic |
Access Restriction | Subscribed |
Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
Subject Keyword | Radar imaging Antenna measurements Sparse matrices PSNR Compressed sensing Pixel Multiple Polarizations Through-the-Wall Radar Imaging Multiple-Measurement Vectors Compressed Sensing |
Content Type | Text |
Resource Type | Article |
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