A Tensor Method of Angle Estimation for Bistatic MIMO Radar in the Presence of Spatially Colored Noise and Strongly Correlated Targets

Shuai Luo, Yuexian Wang, Chuang Han, Yanyun Gong, Jianying Li

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

Abstract

This paper is devoted to the angle estimation problem for bistatic multiple-input multiple-output (MIMO) radar under strongly correlated targets and spatially colored noise scenes. Firstly, the cross-covariance matrix is established by using the independent noise vector after matched filtering in time domain, and the components of spatial colored noise are eliminated. Then, the cross-covariance matrix is rearranged into a third-order tensor, and the reconstructed tensor can be obtained by concatenating two third-order tensors. Finally, the Parallel Factor (PARAFAC) decomposition is utilized to resolve angle estimates. Owing to the reconstructed third-order tensor, angle estimation by the proposed method is more accurate than existing methods and has stronger robustness even though there is a strong correlation between targets. Simulation results verify the advantages of our solutions over its cutting-edge counterparts.

Original languageEnglish
Title of host publicationProceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781665429184
DOIs
StatePublished - 17 Aug 2021
Event2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021 - Xi�an, China
Duration: 17 Aug 202119 Aug 2021

Publication series

NameProceedings of 2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021

Conference

Conference2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021
Country/TerritoryChina
CityXi�an
Period17/08/2119/08/21

Keywords

  • bistatic MIMO radar
  • PARAFAC
  • spatial colored noise
  • strongly correlated targets

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