Maximum Likelihood Angle-Range Estimation in FDA-MIMO radar

Abstract

In this paper, the maximum likelihood (ML) algorithm is exploited to estimate the angles and ranges of targets in frequency diverse array-multiple input multiple output (FDA-MIMO) radar. The maximum likelihood algorithm changes the estimation problem into a multi-dimensional optimization problem, thus we further simplify the multi-dimensional optimization into a series of one-dimensional optimization by alternating projection (AP). The log-likelihood function of angle-range estimation is derived, and the parameters are iteratively optimized by alternating projection. The estimation accuracy and resolution ability of the ML algorithm are evaluated by comparing with the 2D-MUSIC, 2D-TLS-ESPRIT, and the cramer-rao bound (CRB) in the simulations, and the superiority of the ML algorithm is demonstrated.