Low Complexity Moving Target Parameter Estimation For MIMO Radar using 2D-FFT

Abstract

In multiple-input multiple-output radar, to estimate the target parameters, such as the reflection coefficient, spatial location, and Doppler shift, derived cost functions are usually optimized over a grid of points. The performance of such algorithms is directly affected by the grid resolution. Increasing the grid resolution enhances the resolution of the estimator but also increases its computational complexity exponentially. In this work, to estimate the parameters of a target, two reduced complexity optimum performance adaptive algorithms, which are based on Capon and amplitude and phase estimation, are presented. The proposed algorithm exploits the low order two-dimensional fast Fourier transform to determine the sub- optimal estimates of spatial location and Doppler shift, which are then used as the initial points for the derived steepest descent algorithm. In contrast to the grid search based algorithms, the proposed algorithm can optimally estimate on- and off-the-grid targets in very low computational complexity. Simulation results show that the mean-squared estimation error of the proposed estimators achieve the Craḿer-Rao lower bound.