MIMO雷达最大似然参数估计

On the maximum likelihood method for target localization using MIMO radars

多输入多输出(MIMO)雷达使用多个天线同时发射多个独立探测信号,并使用多个天线接收目标回波信号.本文考虑了发射空域分集、相干接收MIMO雷达模型及其最大似然(ML)参数估计方法.基于最大似然准则,本文推导了两种渐近最大似然算法.仿真实验的结果表明,在均匀噪声模型中,其中一种渐近算法与基于延迟求和波束形成的最大似然算法性能接近,而另一种渐近算法性能略差,但具有较低的计算复杂度.而在非均匀噪声模型中,本文所提出的两种渐近最大似然算法的性能均优于基于延迟求和波束形成的最大似然算法.

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.