Bayesian Detection for Distributed MIMO Radar with Non-Orthogonal Waveforms in Non-Homogeneous Clutter

Cengcang Zeng, Fangzhou Wang, Hongbin Li, Mark A. Govoni

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

1 Scopus citations

Abstract

This paper considers target detection in distributed multi-input multi-output (MIMO) radar with non-orthogonal waveforms in non-homogenous clutter. We first present a general signal model for distributed MIMO radar in cluttered environments. To cope with the non-homogenous clutter and possible clutter bandwidth mismatch, the covariance matrix of the disturbance (clutter and noise) signal is modeled as a random matrix following an inverse complex Wishart distribution. Then, we propose three Bayesian detectors, including a non-coherent detector, a coherent detector, and a hybrid detector. The latter is a compromise of the former two, as it forsakes phase estimation needed by the coherent detector, but requires the samples within a coherent processing interval (CPI) to maintain phase coherence that is unnecessary for the non-coherent detector. Simulation results are presented to illustrate the performance of these Bayesian detectors and their non-Bayesian counterparts in non-homogeneous clutter when the clutter bandwidth is known exactly and, respectively, with uncertainty.

Original languageEnglish
Title of host publicationRadarConf23 - 2023 IEEE Radar Conference, Proceedings
ISBN (Electronic)9781665436694
DOIs
StatePublished - 2023
Event2023 IEEE Radar Conference, RadarConf23 - San Antonia, United States
Duration: 1 May 20235 May 2023

Publication series

NameProceedings of the IEEE Radar Conference
Volume2023-May
ISSN (Print)1097-5764
ISSN (Electronic)2375-5318

Conference

Conference2023 IEEE Radar Conference, RadarConf23
Country/TerritoryUnited States
CitySan Antonia
Period1/05/235/05/23

Keywords

  • Bayesian detection
  • Distributed MIMO radar
  • non-homogeneous clutter
  • non-orthogonal waveforms

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