quantized compressive sensing

Dithered quantized compressive sensing with arbitrary RIP matrices

Abstract: Quantized compressive sensing (QCS) deals with the problem of coding compressive measurements of low-complexity signals (e.g., sparse vectors in a given basis, low-rank matrices) with quantized, finite precision representations, i.

1-bit Localization Scheme for Radar using Dithered Quantized Compressed Sensing

Abstract: We present a novel scheme allowing for 2D target localization using highly quantized 1-bit measurements from a Frequency Modulated Continuous Wave (FMCW) radar with two receiving antennas. Quantization of radar signals introduces localization artifacts, we remove this limitation by inserting a dithering on the unquantized observations.

Taking the edge off quantization: projected back projection in dithered compressive sensing

Abstract: Quantized compressive sensing (QCS) deals with the problem of representing compressive signal measurements with finite precision representation, i.e., a mandatory process in any practical sensor design. To characterize the signal reconstruction quality in this framework, most of the existing theoretical analyses lie heavily on the quantization of sub-Gaussian random projections (e.

Consistent Basis Pursuit for Signal and Matrix Estimates in Quantized Compressed Sensing

Abstract: 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.

Consistent Basis Pursuit for Low-Complexity Signal Estimates in Quantized Compressed Sensing

In the context of my participation to the PhD “pré-soutenance” of Marwa Chaffi (CentraleSupélec, Rennes, France).