A RIEMANNIAN-BASED JOINT DESIGN FRAMEWORK OF MIMO RADAR TRANSMIT WAVEFORM AND RECEIVE FILTER VIA INFORMATION THEORY

Jie Li, Yan Huang, Qihui Wu, Arye Nehorai

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

1 Scopus citations

Abstract

In this paper, we explore the joint design of a transmit waveform and receive filter to enhance the detection performance of multiple-input multiple-output (MIMO) radar. Target echoes are assumed to be embedded in signal-dependent interference and colored Gaussian noise. As design metrics, we exploit two information-theoretic criteria, including mutual information (MI) and relative entropy. The joint design problems of MIMO radar associated with different information-theoretic criteria are established as a unified optimization framework within a constant-envelope (CE) constraint. We propose an efficient method based on the Riemannian optimization framework, which transforms the constraint optimization problems into unconstrained problems by leveraging the geometry of the feasible region. Several numerical examples are included to demonstrate the effectiveness of the proposed method.

Original languageEnglish
Title of host publication2024 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Proceedings
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages9831-9835
Number of pages5
ISBN (Electronic)9798350344851
DOIs
StatePublished - 2024
Event49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024 - Seoul, Korea, Republic of
Duration: Apr 14 2024Apr 19 2024

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference49th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2024
Country/TerritoryKorea, Republic of
CitySeoul
Period04/14/2404/19/24

Keywords

  • Information-theoretic criteria
  • joint design
  • MIMO radar
  • Riemannian optimization framework
  • signal-dependent interference

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