Design of multiple-input–multiple-output radar waveforms for Rician target detection

• Published in: IET radar, sonar & navigation Vol. 10; no. 9; pp. 1583 - 1593
• Main Authors: Tang, Bo, Tang, Jun, Zhang, Yu
• Format: Journal Article
• Language: English
• Published: The Institution of Engineering and Technology 01.12.2016
• Subjects: colocated MIMO radar system; concave programming; convergence of numerical methods; Design engineering; detection probability maximisation; Entropy; general signal model; iterative methods; Mathematical models; MIMO radar; MIMO radar design problem; minimisation; minorisation‐maximisation‐based design method; multiple‐input‐multiple‐output radar waveform design problem; Navigation; nonconvex waveform optimisation problem; object detection; optimal Neyman‐Pearson detector; Optimization; Radar; radar detection; relative entropy; Research Article; Rician target detection; Sonar; statistical analysis; statistical MIMO radar system; Waveforms; radar detection; iterative methods; MIMO radar design problem; statistical MIMO radar system; minorisation-maximisation-based design method; object detection; convergence of numerical methods; colocated MIMO radar system; optimal Neyman-Pearson detector; general signal model; Rician target detection; multiple-input-multiple-output radar waveform design problem; nonconvex waveform optimisation problem; entropy; MIMO radar; relative entropy; detection probability maximisation; statistical analysis; minimisation; concave programming
• ISSN: 1751-8784; 1751-8792
• DOI: 10.1049/iet-rsn.2015.0504

This study addresses the waveform design problem of multiple-input–multiple-output (MIMO) radar. The goal is to improve the detection performance for the Rician targets. The authors first establish a general signal model, which can describe the returns by both statistical and colocated MIMO radar systems. Then they derive the associated optimal Neyman–Pearson detector and find the difficulty of optimising the waveforms that maximise the probability of detection (for a fixed probability of false alarm). Alternatively, they employ the relative entropy associated with the detection problem as the design metric. In order to tackle the non-convex waveform optimisation problem, they propose a minorisation-maximisation-based design method. They prove that the proposed method can increase the relative entropy (of the devised waveforms) at each iteration and guarantees convergence. Finally, they include several numerical examples to demonstrate the effectiveness of the proposed method.