Wavelet-Based Compressive Sensing for Point Scatterers
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Date
2015-06
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Mark
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Společnost pro radioelektronické inženýrství
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Abstract
Compressive Sensing (CS) allows for the sam-pling of signals at well below the Nyquist rate but does so, usually, at the cost of the suppression of lower amplitude sig-nal components. Recent work suggests that important infor-mation essential for recognizing targets in the radar context is contained in the side-lobes as well, which are often sup-pressed by CS. In this paper we extend existing techniques and introduce new techniques both for improving the accu-racy of CS reconstructions and for improving the separa-bility of scenes reconstructed using CS. We investigate the Discrete Wavelet Transform (DWT), and show how the use of the DWT as a representation basis may improve the accu-racy of reconstruction generally. Moreover, we introduce the concept of using multiple wavelet-based reconstructions of a scene, given only a single physical observation, to derive re-constructions that surpass even the best wavelet-based CS reconstructions. Lastly, we specifically consider the effect of the wavelet-based reconstruction on classification. This is done indirectly by comparing outputs of different algo-rithms using a variety of separability measures. We show that various wavelet-based CS reconstructions are substan-tially better than conventional CS approaches at inducing (or preserving) separability, and hence may be more useful in classification applications.
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Citation
Radioengineering. 2015 vol. 24, č. 2, s. 621-631. ISSN 1210-2512
http://www.radioeng.cz/fulltexts/2015/15_02_0621_0631.pdf
http://www.radioeng.cz/fulltexts/2015/15_02_0621_0631.pdf
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Peer-reviewed
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en