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

Guohao Sun, Yingkui Zhang, Zhaoke Ning, Yuandong Ji, Zhiquan Ding

Abstract: 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.

External IDs:dblp:journals/spl/SunZNJD25