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Recovering signals with variable sparsity levels from the noisy 1-bit compressive measurements

A. Movahed ; Ashkan Panahi (Institutionen för signaler och system, Signalbehandling) ; M.C. Reed
2014 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2014; Florence; Italy; 4 May 2014 through 9 May 2014 (1520-6149). p. 6454-6458. (2014)
[Konferensbidrag, refereegranskat]

In this paper, we consider the 1-bit compressive sensing reconstruction problem in a scenario that the sparsity level of the signal is unknown and time variant, and the binary measurements are contaminated with the noise. We introduce a new reconstruction algorithm which we refer to as Noise-Adaptive Restricted Step Shrinkage (NARSS). NARSS is superior in terms of performance, complexity and speed of convergence to the algorithms already introduced in the literature for 1-bit compressive sensing reconstruction from the noisy binary measurements.

Nyckelord: compressive sensing (CS) , one bit quantization

Article number 6854847

Denna post skapades 2014-08-22. Senast ändrad 2015-05-08.
CPL Pubid: 201841


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