On the maximum likelihood method for target localization using MIMO radars
A multiple-input multiple-output (MIMO) radar uses multiple antennas to simultaneously transmit multiple independent probing signals, and uses multiple antennas to receive the backscattered signals. The modeling of MIMO radar with transmit spatial diversity and coherent reception is addressed herein. The maximum likelihood (ML) method for parameter estimation using MIMO radars is considered, and two approximate ML algorithms are proposed. In the uniform noise scenario, one of the proposed algorithms performs similarly to the delay-and-sum beamformer which is optimal in the ML sense in single target case, while it outperforms the other proposed approximate ML algorithm at the cost of more computational load. In the non-uniform noise scenario, the proposed approximate ML algorithms both outperform the delay-and-sum beamformer. The efficiency of the proposed methods is validated by the simulation results.
multiple-input multiple-output (mimo) radars parameter estimation maximum-likelihood