Compressed Statistical Testing and Application to Radar

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Compressed Statistical Testing and Application to Radar

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Title: Compressed Statistical Testing and Application to Radar
Author: Wicks, Michael C.; Kung, H.T. T.; Chen, Hsieh-Chung

Note: Order does not necessarily reflect citation order of authors.

Citation: Chen, Hsieh-Chung, H. T. Kung, and Michael Wicks. 2012. Compressed statistical testing and application to radar. Paper presented at the 1st International Workshop on Compressed Sensing Applied to Radar (CoSeRa 2012) Bonn, Germany, May 14-16, 2012.
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Abstract: We present compressed statistical testing (CST) with an illustrative application to radar target detection. We characterize an optimality condition for a compressed domain test to yield the same result as the corresponding test in the uncompressed domain. We demonstrate by simulation that under high SNR, a likelihood ratio test with compressed samples at 3.3x or even higher compression ratio can achieve detection performance comparable to that with uncompressed data. For example, our compressed domain Sample Matrix Inversion test for radar target detection can achieve constant false alarm rate (CFAR) performance similar to the corresponding test in the raw data domain. By exploiting signal sparsity in the target and interference returns, compressive sensing based CST can incur a much lower processing cost in statistical training and decision making, and can therefore enable a variety of distributed applications such as target detection on resource limited mobile devices.
Other Sources: http://www.eecs.harvard.edu/~htk/publication/2012-cosera-chen-kung-wicks.pdf
Terms of Use: This article is made available under the terms and conditions applicable to Other Posted Material, as set forth at http://nrs.harvard.edu/urn-3:HUL.InstRepos:dash.current.terms-of-use#LAA
Citable link to this page: http://nrs.harvard.edu/urn-3:HUL.InstRepos:10007899

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  • FAS Scholarly Articles [6464]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University
 
 

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