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arXiv · 1302.1419

Blind One-Bit Compressive Sampling

Lixin Shen·Bruce W. Suter
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Abstract

The problem of 1-bit compressive sampling is addressed in this paper. We introduce an optimization model for reconstruction of sparse signals from 1-bit measurements. The model targets a solution that has the least l0-norm among all signals satisfying consistency constraints stemming from the 1-bit measurements. An algorithm for solving the model is developed. Convergence analysis of the algorithm is presented. Our approach is to obtain a sequence of optimization problems by successively approximating the l0-norm and to solve resulting problems by exploiting the proximity operator. We examine the performance of our proposed algorithm and compare it with the binary iterative hard thresholding (BIHT) [10] a state-of-the-art algorithm for 1-bit compressive sampling reconstruction. Unlike the BIHT, our model and algorithm does not require a prior knowledge on the sparsity of the signal. This makes our proposed work a promising practical approach for signal acquisition.

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Lixin Shen, Bruce W. Suter. 2013-02-06. Blind One-Bit Compressive Sampling. https://arxiv.org/abs/1302.1419

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