Target Localization With a Coprime Multistatic MIMO Radar via Coupled Canonical Polyadic Decomposition Based on Joint EVD

This paper addresses target localization using a multistatic multiple-input multiple-output (MIMO) radar system with coprime L-shaped receive arrays (CLsA). A target localization method is proposed by modeling the observed signals as tensors that admit a coupled canonical polyadic decomposition (C-CPD) model without matched filtering. It consists of a novel joint eigenvalue decomposition (J-EVD) based (semi-)algebraic algorithm, and a post-processing approach to determine the target locations by fusing the direction-of-arrival estimates extracted from J-EVD-based C-CPD results. Particularly, by leveraging the rotational invariance of Vandermonde structure in CLsA, we convert the C-CPD problem into a J-EVD problem, significantly reducing its computational complexity. Experimental results show that our method outperforms existing tensor-based ones.

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Publication Details

Published
2025-03-12
DOI
https://doi.org/10.1109/icassp49660.2025.10887778
Primary Topic
Advanced SAR Imaging Techniques
Type
article
Field-Weighted Citation Impact
0.00

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article

Target Localization With a Coprime Multistatic MIMO Radar via Coupled Canonical Polyadic Decomposition Based on Joint EVD

Guo-Zhao Liao, Xiao‐Feng Gong, Hing Cheung So, Wei Liu
Advanced SAR Imaging Techniques
article

Target Localization With a Coprime Multistatic MIMO Radar via Coupled Canonical Polyadic Decomposition Based on Joint EVD

Guo-Zhao Liao, Xiao‐Feng Gong, Hing Cheung So, Wei Liu
article en

Abstract

This paper addresses target localization using a multistatic multiple-input multiple-output (MIMO) radar system with coprime L-shaped receive arrays (CLsA). A target localization method is proposed by modeling the observed signals as tensors that admit a coupled canonical polyadic decomposition (C-CPD) model without matched filtering. It consists of a novel joint eigenvalue decomposition (J-EVD) based (semi-)algebraic algorithm, and a post-processing approach to determine the target locations by fusing the direction-of-arrival estimates extracted from J-EVD-based C-CPD results. Particularly, by leveraging the rotational invariance of Vandermonde structure in CLsA, we convert the C-CPD problem into a J-EVD problem, significantly reducing its computational complexity. Experimental results show that our method outperforms existing tensor-based ones.

Hong Kong Polytechnic University (HK), City University of Hong Kong (HK), Dalian University of Technology (CN)
National Natural Science Foundation of China
Affordable and clean energy
Openalex Percentile: Top 94%
Advanced SAR Imaging Techniques
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