A survey on one-bit compressed sensing: theory and applications

doi:10.1007/s11704-017-6132-7

Front. Comput. Sci.

2018, Vol. 12

Issue (2) : 217-230 https://doi.org/10.1007/s11704-017-6132-7

REVIEW ARTICLE

A survey on one-bit compressed sensing: theory and applications

Zhilin LI(

), Wenbo XU, Xiaobo ZHANG, Jiaru LIN

Key Lab of Universal Wireless Communications,Ministry of Education, Beijing University of Posts and Telecommunications, Beijing 100876, China

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Abstract

In the past few decades, with the growing popularity of compressed sensing (CS) in the signal processing field, the quantization step in CS has received significant attention. Current research generally considers multi-bit quantization. For systems employing quantization with a sufficient number of bits, a sparse signal can be reliably recovered using various CS reconstruction algorithms.

Recently, many researchers have begun studying the onebit case for CS. As an extreme case of CS, onebit CS preserves only the sign information of measurements, which reduces storage costs and hardware complexity. By treating one-bit measurements as sign constraints, it has been shown that sparse signals can be recovered using certain reconstruction algorithms with a high probability. Based on the merits of one-bit CS, it has been widely applied to many fields, such as radar, source location, spectrum sensing, and wireless sensing network.

In this paper, the characteristics of one-bit CS and related works are reviewed. First, the framework of one-bit CS is introduced. Next, we summarize existing reconstruction algorithms. Additionally, some extensions and practical applications of one-bit CS are categorized and discussed. Finally, our conclusions and the further research topics are summarized.

Keywords compressed sensing one-bit quantization sign information support consistency

Corresponding Author(s): Zhilin LI

Just Accepted Date: 05 January 2017 Online First Date: 06 March 2018 Issue Date: 22 March 2018

Cite this article:

Zhilin LI,Wenbo XU,Xiaobo ZHANG, et al. A survey on one-bit compressed sensing: theory and applications[J]. Front. Comput. Sci., 2018, 12(2): 217-230.

URL:

https://academic.hep.com.cn/fcs/EN/10.1007/s11704-017-6132-7
https://academic.hep.com.cn/fcs/EN/Y2018/V12/I2/217

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