Abstract —Waveform optimization for Multi-Input Multi-Output (MIMO) radar, which usually depends on the initial parameter estimates (i.e., some prior information on the target of interest and scenario), is often sensitive to estimation errors and uncertainty in the parameters. Robust waveform design attempts to systematically alleviate this sensitivity by explicitly incorporating a parameter uncertainty model in the optimization problem. In this paper, we consider the robust waveform optimization to improve the worst-case detection performance over a convex uncertainty model. An iterative algorithm is proposed to optimize the Waveform Covariance Matrix (WCM) for maximizing the worst-case output Signal-Interference-Noise-Ratio (SINR) such that the worst-case detection performance can be improved. Each iteration step in the proposed algorithm can be reformulated as a Semidefinite Programming (SDP) problem, which can be solved very efficiently. Numerical results show that the worst-case detection performance can be improved considerably by the proposed method compared to those of the non-robust method and uncorrelated waveforms.

Index Terms—Multi-Input Multi-Output (MIMO) radar, robust waveform optimization, convex optimization, target detection, Semidefinite Programming (SDP)

Cite: Hongfeng Wang and Hongyan Wang, “Robust Waveform Optimization for MIMO Radar to Improve the Worst-Case Detection Performance," Journal of Communications, vol. 10, no. 12, pp.983-989, 2015. Doi: 10.12720/jcm.10.12.983-989