Asymptotic performance analysis of the conjugate gradient reduced-rank adaptive detector

Zhu Chen, Hongbin Li, Muralidhar Rangaswamy

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

2 Scopus citations

Abstract

We consider an adaptive reduced-rank CG-AMF detector, obtained by using the conjugate gradient (CG) algorithm to solve for the weight vector of the adaptive matched filter (AMF). We examine the output signal-to-interference-and- noise ratio (SINR) of the CG-AMF detector in the presence of strong clutter/interference. An asymtotic expression of the probability density function of the output SINR is obtained. Numerical results show that for a fixed training size, the CG-AMF detector often reaches its peak output SINR with a lower rank compared with the other reduced-rank detectors, which implies that the CG-AMF detector has lower computational complexity and less training requirement

Original languageEnglish
Title of host publicationIEEE Radar Conference 2013
Subtitle of host publication"The Arctic - The New Frontier", RadarCon 2013
DOIs
StatePublished - 2013
Event2013 IEEE Radar Conference: "The Arctic - The New Frontier", RadarCon 2013 - Ottawa, ON, Canada
Duration: 29 Apr 20133 May 2013

Publication series

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

Conference

Conference2013 IEEE Radar Conference: "The Arctic - The New Frontier", RadarCon 2013
Country/TerritoryCanada
CityOttawa, ON
Period29/04/133/05/13

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