Knowledge-aided parametric GLRT for space-time adaptive processing

Pu Wang, Hongbin Li, Braham Himed

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

2 Scopus citations

Abstract

In this paper, we consider knowledge-aided space-time adaptive processing (KA-STAP) with a parametric approach, where disturbances in both test and training signals are modeled as a multichannel auto-regressive (AR) model. The a priori knowledge is incorporated into the detection problem through a stochastic signal model, where the spatial covariance matrix of the disturbance is assumed random. According to this model, a Bayesian version of the parametric generalized likelihood ratio test (PGLRT) is developed in a two-step approach, which is referred to as the KA-PGLRT. Interestingly, the KA-PGLRT employs a colored loading approach for estimation of the spatial covariance matrix of the test signal. Simulation results show that the KA-PGLRT can obtain better detection performance over other parametric detectors.

Original languageEnglish
Title of host publicationConference Record of the 44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Pages1981-1985
Number of pages5
DOIs
StatePublished - 2010
Event44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010 - Pacific Grove, CA, United States
Duration: 7 Nov 201010 Nov 2010

Publication series

NameConference Record - Asilomar Conference on Signals, Systems and Computers
ISSN (Print)1058-6393

Conference

Conference44th Asilomar Conference on Signals, Systems and Computers, Asilomar 2010
Country/TerritoryUnited States
CityPacific Grove, CA
Period7/11/1010/11/10

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