Joint sparse with generalized orthogonal matching pursuit for off-grid wideband DOA estimation

Xingchen Liu, Haiyan Wang, Xiaohong Shen, Haitao Dong, Haixian Jing

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

1 Scopus citations

Abstract

Off-Grid wideband DOA estimation under low signal-to-noise ratio (SNR) and limited snapshots is a challenging problem for sonar array signal processing. In this paper, we proposed a novel joint sparse method with Generalized Orthogonal Matching Pursuit (J-GOMP). The method is on the basis of classical OMP with a double steering model parameters optimization. With the chosen of K terms in the largest absolute value of the inner product, the J-GOMP algorithm can achieve faster convergence speed. The detailed algorithm realization steps are given. Simulations are conducted in comparison with classical MUSIC and OMP algorithms. The results show the proposed J-GOMP has better resolution performance under low SNR with a single snapshot.

Original languageEnglish
Title of host publication2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728117072
DOIs
StatePublished - Sep 2019
Event2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019 - Dalian, Liaoning, China
Duration: 20 Sep 201922 Sep 2019

Publication series

Name2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019

Conference

Conference2019 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2019
Country/TerritoryChina
CityDalian, Liaoning
Period20/09/1922/09/19

Keywords

  • Direction of arrival estimation
  • J-GOMP
  • Joint sparse recovery methods
  • Off-grid array steering model

Fingerprint

Dive into the research topics of 'Joint sparse with generalized orthogonal matching pursuit for off-grid wideband DOA estimation'. Together they form a unique fingerprint.

Cite this