Parametric multichannel adaptive signal detection: Exploiting persymmetric structure

Pu Wang, Zafer Sahinoglu, Man On Pun, Hongbin Li

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

Abstract

This paper considers a parametric approach for adaptive multichannel signal detection, where the disturbance is modeled by a multichannel auto-regressive (AR) process. Motivated by the fact that a symmetric antenna geometry usually yields a persymmetric structure on the covariance matrix of disturbance, a new persymmetric AR (PAR) modeling for the disturbance is proposed and, accordingly, a persymmetric parametric adaptive matched filter (Per-PAMF) is developed. The developed Per-PAMF, while allowing a simple implementation like the traditional PAMF, extends the PAMF by developing the maximum likelihood (ML) estimation of unknown nuisance (disturbance-related) parameters under the persymmetric constraint. Numerical results show that the Per-PAMF provides significantly better detection performance than the conventional PAMF and other non-parametric detectors when the number of training signals is limited.

Original languageEnglish
Title of host publication2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Proceedings
Pages2449-2452
Number of pages4
DOIs
StatePublished - 2012
Event2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012 - Kyoto, Japan
Duration: 25 Mar 201230 Mar 2012

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2012 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2012
Country/TerritoryJapan
CityKyoto
Period25/03/1230/03/12

Keywords

  • Multichannel signal processing
  • adaptive matched filter
  • auto-regressive process
  • maximum likelihood estimation
  • multichannel
  • persymmetry

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