Performance Bound Optimization for MIMO Radar Direction Finding With MUSIC
Wenjun Wu, Bo Tang, Ran Tao
Abstract
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.