Robust Joint Design of MIMO Radar Waveform and Filter via Structured Covariance Matrix

• Published in: IEEE signal processing letters Vol. 32; pp. 2539 - 2543
• Main Authors: Sun, Guohao, Zhang, Yingkui, Ning, Zhaoke, Ji, Yuandong, Ding, Zhiquan
• Format: Journal Article
• Language: English
• Published: New York IEEE 2025; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: ADMM algorithm; Clutter; Covariance matrices; Covariance matrix; Filtering algorithms; Information filters; insufficient prior information; Iterative methods; Mathematical models; MIMO radar; Optimization; Performance enhancement; Radiators; structured covariance matrix; Sun; Uncertainty; Vectors; waveform and filter design; Waveforms
• ISSN: 1070-9908; 1558-2361
• DOI: 10.1109/LSP.2025.3581145

Radar waveform design performance is compromised by insufficient prior information regarding radiators and clutter. This letter addresses this challenge by leveraging structured covariance matrices to enhance the robustness of multiple-input multiple-output (MIMO) radar waveform design. We explore the joint optimization of MIMO radar transmit waveforms and receive filters in environments with radiators and clutter, even in the absence of adequate prior information. To tackle this issue, an iterative method is employed under worst-case assumptions regarding the covariance matrices of the radiators and clutter. By applying the alternating direction method of multipliers (ADMM) algorithm, this letter introduces a novel approach for designing waveforms and structuring the covariance matrices' parameters in spectrally crowded and cluttered environments. Simulation results demonstrate that the proposed method significantly improves performance in mismatched environments.