EconPapers    
Economics at your fingertips  
 

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

Tianyi Jia (), Haiyan Wang () and Xiaohong Shen ()
Additional contact information
Tianyi Jia: Northwestern Polytechnical University
Haiyan Wang: Northwestern Polytechnical University
Xiaohong Shen: Northwestern Polytechnical University

Telecommunication Systems: Modelling, Analysis, Design and Management, 2020, vol. 74, issue 4, No 10, 537 pages

Abstract: 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: DOA estimation; Compressed sensing; RIPless theory (search for similar items in EconPapers)
Date: 2020
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://link.springer.com/10.1007/s11235-020-00676-8 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:telsys:v:74:y:2020:i:4:d:10.1007_s11235-020-00676-8

Ordering information: This journal article can be ordered from
http://www.springer.com/journal/11235

DOI: 10.1007/s11235-020-00676-8

Access Statistics for this article

Telecommunication Systems: Modelling, Analysis, Design and Management is currently edited by Muhammad Khan

More articles in Telecommunication Systems: Modelling, Analysis, Design and Management from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().

 
Page updated 2025-03-20
Handle: RePEc:spr:telsys:v:74:y:2020:i:4:d:10.1007_s11235-020-00676-8