A study of compressed sensing single-snapshot DOA estimation based on the RIPless theory

Tianyi Jia; Haiyan Wang; Xiaohong Shen

doi:10.1007/s11235-020-00676-8

A study of compressed sensing single-snapshot DOA estimation based on the RIPless theory

Tianyi Jia
, Haiyan Wang
, Xiaohong Shen

School of Marine Science and Technology

Northwestern Polytechnical University Xian
Ministry of Industry and Information Technology
Shaanxi University of Science and Technology

Research output: Contribution to journal › Letter › peer-review

8 Scopus citations

Abstract

We explore the RIPless theory for the analysis of single snapshot DOA estimation with uniform linear array using the compressed sensing technique. Starting with a sparse signal recovery model constructed for single snapshot DOA estimation, we prove the isotropy property and incoherence property are fulfilled for the estimation problem. A vital proposition is obtained using the RIPless theory, which establishes the fundamental relationship of the probability of recovery with the number of targets and sensors.

Original language	English
Pages (from-to)	531-537
Number of pages	7
Journal	Telecommunication Systems
Volume	74
Issue number	4
DOIs	https://doi.org/10.1007/s11235-020-00676-8
State	Published - 1 Aug 2020

Keywords

Compressed sensing
DOA estimation
RIPless theory

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10.1007/s11235-020-00676-8

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abstract = "We explore the RIPless theory for the analysis of single snapshot DOA estimation with uniform linear array using the compressed sensing technique. Starting with a sparse signal recovery model constructed for single snapshot DOA estimation, we prove the isotropy property and incoherence property are fulfilled for the estimation problem. A vital proposition is obtained using the RIPless theory, which establishes the fundamental relationship of the probability of recovery with the number of targets and sensors.",

keywords = "Compressed sensing, DOA estimation, RIPless theory",

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AU - Wang, Haiyan

AU - Shen, Xiaohong

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N2 - We explore the RIPless theory for the analysis of single snapshot DOA estimation with uniform linear array using the compressed sensing technique. Starting with a sparse signal recovery model constructed for single snapshot DOA estimation, we prove the isotropy property and incoherence property are fulfilled for the estimation problem. A vital proposition is obtained using the RIPless theory, which establishes the fundamental relationship of the probability of recovery with the number of targets and sensors.

AB - We explore the RIPless theory for the analysis of single snapshot DOA estimation with uniform linear array using the compressed sensing technique. Starting with a sparse signal recovery model constructed for single snapshot DOA estimation, we prove the isotropy property and incoherence property are fulfilled for the estimation problem. A vital proposition is obtained using the RIPless theory, which establishes the fundamental relationship of the probability of recovery with the number of targets and sensors.

KW - Compressed sensing

KW - DOA estimation

KW - RIPless theory

UR - https://www.scopus.com/pages/publications/85085999954

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A study of compressed sensing single-snapshot DOA estimation based on the RIPless theory

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Keywords

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