Performance Bound Optimization for MIMO Radar Direction Finding With MUSIC

The multiple signal classification (MUSIC) algorithm has been widely applied in direction finding with multiple-input-multiple-output (MIMO) radar. To enhance the angle estimation performance of the MUSIC algorithm, we investigate a waveform-design-based approach and formulate a waveform optimization problem based on minimizing the asymptotic estimation error bound of MUSIC. To tackle the peak-to-average-power-ratio (PAPR)-constrained waveform design problem, we develop two iterative algorithms. The first algorithm is a two-step approach, in which the low-PAPR waveforms are synthesized from the optimal waveform covariance matrix obtained in the first step. The second algorithm is developed based on the minorization-maximization technique, in which an approximated objective function is decreased iteratively. Numerical examples demonstrate the superior performance of the waveforms synthesized by the proposed algorithms.