Low complexity joint estimation of reflection coefficient, spatial location, and Doppler shift for MIMO-radar by exploiting 2D-FFT

In multiple-input multiple-output (MIMO) radar, to estimate the reflection coefficient, spatial location, and Doppler shift of a target, maximum-likelihood (ML) estimation yields the best performance. For this problem, the ML estimation requires the joint estimation of spatial location and Doppler shift, which is a two dimensional search problem. Therefore, the computational complexity of ML estimation is prohibitively high. In this work, to estimate the parameters of a target, a reduced complexity optimum performance algorithm is proposed, which allow two dimensional fast Fourier transform to jointly estimate the spatial location and Doppler shift. To asses the performances of the proposed estimators, the Cramér-Rao-lower-bound (CRLB) is derived. Simulation results show that the mean square estimation error of the proposed estimators achieve the CRLB.