Work

Computing One-Bit Compressive Sensing via Double-Sparsity Constrained Optimization

Year: 2022

Type: article

Abstract: One-bit compressive sensing gains its popularity in signal processing and communications due to its low storage costs and low hardware complexity. However, it has been a challenging task to recover the signal only by exploiting the one-bit (the sign) information. In this paper, we appropriately formulate the one-bit compressive sensing into a double-sparsity constrained optimization problem. The first-order optimality conditions for this nonconvex and discontinuous problem are established via the newly introduced$\tau$-stationarity, based on which, a gradient projection subspace pursuit (GPSP) algorithm is developed. It is proven that GPSP can converge globally and terminate within finite steps. Numerical experiments have demonstrated its excellent performance in terms of a high order of accuracy with a high computational speed.

Source: IEEE Transactions on Signal Processing

DOI: 10.1109/tsp.2022.3156911

Authors: Shenglong Zhou, Ziyan Luo, Naihua Xiu, Geoffrey Ye Li

Institutions: Imperial College London, Beijing Jiaotong University

Language: English

Topic: Sparse and Compressive Sensing Techniques

Subfield: Computational Mechanics

Field: Engineering

Domain: Physical Sciences

Open Access status: green

Funders: National Natural Science Foundation of China, Natural Science Foundation of Beijing Municipality

Awards: G2363803437, G297500294