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Time-frequency DOA estimation based on Radon-Wigner transform

  • Hu Hang*
  • *Corresponding author for this work
  • School of Electronics and Information Engineering, Harbin Institute of Technology

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

Abstract

In the studies of DOA estimation based on time-frequency analysis, eliminating cross interference terms is the key to improve DOA estimation performance for the multi-signal sources situation. PWVD is the most commonly used time-frequency analysis tool for suppressing cross interference terms in WVD. However, the cross terms improvement of PWVD is not so ideal and the estimated results are not so reliable, SPWVD can suppress cross terms better, but the time-frequency concentration performance of signal drops largely. This paper employs RWT (Radon-Wigner Transform) as time-frequency transform tool which is very suitable to describe LFM signals. This method can suppress cross terms in the background of multi sources effectively, possess good time-frequency concentration performance, and consequently it can improve the accuracy of DOA estimation much more greatly. Simulation results demonstrate that the performances of DOA estimation based on RWT are notably improved compared with those ones based on PWVD and SPWVD.

Original languageEnglish
Title of host publication8th International Conference on Signal Processing, ICSP 2006
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Print)0780397371, 9780780397378
DOIs
StatePublished - 2006
Externally publishedYes
Event8th International Conference on Signal Processing, ICSP 2006 - Guilin, China
Duration: 16 Nov 200620 Nov 2006

Publication series

NameInternational Conference on Signal Processing Proceedings, ICSP
Volume1

Conference

Conference8th International Conference on Signal Processing, ICSP 2006
Country/TerritoryChina
CityGuilin
Period16/11/0620/11/06

Keywords

  • Cross terms suppression
  • DOA estimation
  • Radon-Wigner transform
  • Time-frequency analysis

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