Bayesian parametric approach for multichannel adaptive signal detection

Pu Wang, Hongbin Li, Braham Himed

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

2 Scopus citations

Abstract

This paper considers the problem of space-time adaptive processing (STAP) in non-homogeneous environments, where the disturbance covariance matrices of the training and test signals are assumed random and different with each other. A Bayesian detection statistic is proposed by incorporating the randomness of the disturbance covariance matrices, utilizing a priori knowledge, and exploring the inherent Block-Toeplitz structure of the spatial-temporal covariance matrix. Specifically, the Block-Toeplitz structure of the covariance matrix allows us to model the training signals as a multichannel auto-regressive (AR) process and hence, develop the Bayesian parametric adaptive matched filter (B-PAMF) to mitigate the training requirement and alleviate the computational complexity. Simulation using both simulated multichannel AR data and the challenging KASSPER data validates the effectiveness of the B-PAMF in non-homogeneous environments.

Original languageEnglish
Title of host publication2010 IEEE Radar Conference
Subtitle of host publicationGlobal Innovation in Radar, RADAR 2010 - Proceedings
Pages838-841
Number of pages4
DOIs
StatePublished - 2010
EventIEEE International Radar Conference 2010, RADAR 2010 - Washington DC, United States
Duration: 10 May 201014 May 2010

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659

Conference

ConferenceIEEE International Radar Conference 2010, RADAR 2010
Country/TerritoryUnited States
CityWashington DC
Period10/05/1014/05/10

Keywords

  • Bayesian detection
  • Non-homogeneous environments
  • Parametric adaptive matched filter
  • Space-time adaptive signal processing

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