Enhanced DOD and DOA estimations in coprime MIMO radar: Modified matrix pencil method

Recent research indicates that coprime multiple-input multiple-output (MIMO) radar systems enhance target detection and parameter estimation capabilities due to their unique array configurations. However, despite these advantages, effectively managing scenarios with both coherent and uncorrelated targets requires a delicate balance between computational efficiency and performance accuracy. In this paper, we propose an innovative approach for the joint estimation of the direction of departure (DOD) and direction of arrival (DOA) in coprime MIMO radar systems capable of effectively handling both coherent and uncorrelated targets. We first construct an extended virtual uniform rectangular array (URA) by employing array interpolation, which enhances the system's resolution capabilities. Next, we apply a low-rank structured matrix recovery technique to tackle inherent rank deficiency issues in coherent targets. This approach allows us to estimate the parameters of these targets accurately. We use the full-rank covariance matrix to directly apply the modified matrix pencil (MMP) method for estimating DOD and DOA. This dual-faceted approach automatically pairs estimated parameters without separating processing paths for coherent and uncorrelated targets. Comprehensive simulations indicate the effectiveness of our proposed algorithm in managing mixed target scenarios. It achieves high estimation accuracy and resolution while maintaining computational efficiency.

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Publication Details

Journal
Digital Signal Processing
Published
2025-01-09
DOI
https://doi.org/10.1016/j.dsp.2024.104977
Citations
9
Primary Topic
Radar Systems and Signal Processing
Type
article
Field-Weighted Citation Impact
44.94

Funders

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article

Enhanced DOD and DOA estimations in coprime MIMO radar: Modified matrix pencil method

Xin Lai, Farman Ali, M.O. Ahmad, Xiaofei Zhang
9 citations
Digital Signal Processing
Radar Systems and Signal Processing
44.94
article

Enhanced DOD and DOA estimations in coprime MIMO radar: Modified matrix pencil method

Xin Lai, Farman Ali, M.O. Ahmad, Xiaofei Zhang
article en
9 citations

Abstract

Recent research indicates that coprime multiple-input multiple-output (MIMO) radar systems enhance target detection and parameter estimation capabilities due to their unique array configurations. However, despite these advantages, effectively managing scenarios with both coherent and uncorrelated targets requires a delicate balance between computational efficiency and performance accuracy. In this paper, we propose an innovative approach for the joint estimation of the direction of departure (DOD) and direction of arrival (DOA) in coprime MIMO radar systems capable of effectively handling both coherent and uncorrelated targets. We first construct an extended virtual uniform rectangular array (URA) by employing array interpolation, which enhances the system's resolution capabilities. Next, we apply a low-rank structured matrix recovery technique to tackle inherent rank deficiency issues in coherent targets. This approach allows us to estimate the parameters of these targets accurately. We use the full-rank covariance matrix to directly apply the modified matrix pencil (MMP) method for estimating DOD and DOA. This dual-faceted approach automatically pairs estimated parameters without separating processing paths for coherent and uncorrelated targets. Comprehensive simulations indicate the effectiveness of our proposed algorithm in managing mixed target scenarios. It achieves high estimation accuracy and resolution while maintaining computational efficiency.

Digital Signal ProcessingVol. 159
Ministry of Industry and Information Technology (CN), Nanjing University of Aeronautics and Astronautics (CN)
Nanjing University of Aeronautics and Astronautics
Zero hunger
Openalex Percentile: Top 0%
Radar Systems and Signal Processing
44.94
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Enhanced DOD and DOA estimations in coprime MIMO radar: Modified matrix pencil method — Xin Lai, Farman Ali, et al. · Digital Signal Processing (2025) | TGRS Research Map | TGRS