Persymmetric parametric adaptive matched filter for multichannel adaptive signal detection

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

Research output: Contribution to journalArticlepeer-review

66 Scopus citations

Abstract

This correspondence considers a parametric approach for multichannel adaptive signal detection in Gaussian disturbance which can be modeled as a multichannel autoregressive (AR) process and, moreover, possesses a persymmetric structure induced by a symmetric antenna geometry. By introducing the persymmetric AR (PAR) modeling for the disturbance, a persymmetric parametric adaptive matched filter (Per-PAMF) is proposed. The developed Per-PAMF extends the classical PAMF by exploiting the underlying persymmetric properties and, hence, improves the detection performance in training-limited scenarios. The performance of the proposed Per-PAMF is examined by the Monte Carlo simulations and simulation results demonstrate the effectiveness of the Per-PAMF compared with the conventional PAMF and nonparametric detectors.

Original languageEnglish
Article number6166358
Pages (from-to)3322-3328
Number of pages7
JournalIEEE Transactions on Signal Processing
Volume60
Issue number6
DOIs
StatePublished - Jun 2012

Keywords

  • Maximum likelihood estimation
  • Multichannel adaptive signal detection
  • Multichannel autoregressive process
  • Parametric approach
  • Persymmetry

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