Optimal Target Detection for Random Channel Matrix-Based Cognitive Radar/Sonar

Touseef Ali, Christ D. Richmond

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Conventional techniques for characterizing clutter depend on covariance-based statistical modeling. This presents a disadvantage to cognitive radar/sonar since optimizing waveform design becomes highly nonconvex. Modeling the clutter and target responses via random transfer functions known as channel matrices simplifies this waveform optimization problem. The goal of this paper is to explore the optimal receive architectures for target detection that emerge when these channel matrices are modeled as deterministic, and then as random using a Ricean channel model. A likelihood ratio test (LRT) is derived yielding the well-known coherent matched filter, and an average LRT (ALRT) test is derived using Bayesian integration. The detection performance of these receivers is assessed and compared via standard analyses yielding receiver operating characteristic (ROC) curves. It is shown that the optimal ALRT is not strictly a linear function of the data.

Original languageEnglish (US)
Title of host publication2021 IEEE Radar Conference
Subtitle of host publicationRadar on the Move, RadarConf 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728176093
StatePublished - May 7 2021
Event2021 IEEE Radar Conference, RadarConf 2021 - Atlanta, United States
Duration: May 8 2021May 14 2021

Publication series

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


Conference2021 IEEE Radar Conference, RadarConf 2021
Country/TerritoryUnited States


  • Average Likelihood Ratio Test (ALRT)
  • Likelihood Ratio Test (LRT)
  • Receiver Operating Characteristics (ROC)
  • radar detection

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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