Conjugate gradient parametric adaptive matched filter

Chaoshu Jiang, Hongbin Li, Muralidhar Rangaswamy

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

8 Scopus citations

Abstract

The parametric adaptive matched filter (PAMF) detector for space-time adaptive processing (STAP) detection is re-examined in this paper. Originally, the PAMF detector was introduced by using a multichannel autoregressive (AR) parametric model for the disturbance signal in STAP detection. While the parametric approach brings in benefits such as significantly reduced training and computational requirements as compared with fully adaptive STAP detectors, the PAMF detector as a reduced-dimensional solution remains unclear. This paper employs the conjugate-gradient (CG) algorithm to solve the linear prediction problem arising in the PAMF detector. It is shown that CG yields not only a new computationally efficient implementation of the PAMF detector, but it also offers new perspectives of PAMF as a reduced-rank subspace detector. The CG algorithm is first introduced to provide alternative implementations for the matched filter (MF) and parametric matched filter (PMF) when the covariance matrix of the disturbance signal is known. It is then extended to the adaptive case where the covariance matrix is estimated from training data. Important issues such as unknown model order and convergence rate are discussed. Performance of the proposed CG-PAMF detector is examined by using the KASSPER and other computer generated data.

Original languageEnglish
Title of host publication2010 IEEE Radar Conference
Subtitle of host publicationGlobal Innovation in Radar, RADAR 2010 - Proceedings
Pages740-745
Number of pages6
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

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