Consistent Basis Pursuit for Signal and Matrix Estimates in Quantized Compressed Sensing
نویسندگان
چکیده
منابع مشابه
Consistent Signal and Matrix Estimates in Quantized Compressed Sensing
This paper focuses on the estimation of low-complexity signals when they are observed through M uniformly quantized compressive observations. Among such signals, we consider 1-D sparse vectors, low-rank matrices, or compressible signals that are well approximated by one of these two models. In this context, we prove the estimation efficiency of a variant of Basis Pursuit Denoise, called Consist...
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We consider the Compressed Sensing problem. We have a large under-determined set of noisy measurements Y = GX+N, where X is a sparse signal and G is drawn from a random ensemble. In our previous work, we had shown that a signal-to-noise ratio, SNR = O(log n) is necessary and sufficient for support recovery from an information-theoretic perspective. In this paper we present a linear programming ...
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ژورنال
عنوان ژورنال: IEEE Signal Processing Letters
سال: 2016
ISSN: 1070-9908,1558-2361
DOI: 10.1109/lsp.2015.2497543