Exploiting persymmetry for adaptive detection in distributed MIMO radar

Jun Liu, Hongbin Li, Braham Himed

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

Abstract

We consider the adaptive detection problem in colored Gaussian noise with unknown persymmetric covariance matrix in a multiple-input-multiple-output (MIMO) radar with spatially dispersed antennas. To this end, a set of secondary data for each transmit-receive pair is assumed to be available. MIMO versions of the persymmetric generalized likelihood ratio test (MIMO-PGLRT) detector and the persymmetric sampler matrix inversion (MIMO-PSMI) detector are proposed. Compared to the MIMO-PGLRT detector, the MIMO-PSMI detector has a simple form and is computationally more efficient. Numerical examples are provided to demonstrate that the proposed two detection algorithms can significantly alleviate the requirement of the amount of secondary data, and allow for a noticeable improvement in detection performance.

Original languageEnglish
Title of host publication2016 24th European Signal Processing Conference, EUSIPCO 2016
Pages2091-2095
Number of pages5
ISBN (Electronic)9780992862657
DOIs
StatePublished - 28 Nov 2016
Event24th European Signal Processing Conference, EUSIPCO 2016 - Budapest, Hungary
Duration: 28 Aug 20162 Sep 2016

Publication series

NameEuropean Signal Processing Conference
Volume2016-November
ISSN (Print)2219-5491

Conference

Conference24th European Signal Processing Conference, EUSIPCO 2016
Country/TerritoryHungary
CityBudapest
Period28/08/162/09/16

Keywords

  • Adaptive detection
  • Multiple-input-multipleoutput (MIMO) radar
  • Persymmetry

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