单基地MIMO雷达中相干目标的波达角和多普勒频率快速联合估计算法
Computation Efficient Joint Angle and Doppler Frequency Estimation of Coherent Targets in Monostatic MIMO Radar

针对单基地MIMO中相干目标的波达角（Direction-of-arrival，DOA）和多普勒频率联合估计问题，提出了一种降维-前向平滑-传播算子算法（Reduced dimension-forward spatial smoothing-propagator method，RD-FSS-PM）。该算法首先通过对接收信号进行降维变换以降低复杂度，继而利用前向平滑技术（Forward spatial smoothing，FSS）实现解相干，最后通过传播算子算法（Propagator method，PM）实现了对相干目标的波达角和多普勒频率联合估计，且需额外配对。与传统的FSS-PM算法相比，所提算法波达角估计性能提升，多普勒频率估计性能接近而复杂度大大降低。本文同时分析了算法的理论均方误差（Mean squared error，MSE）和单基地MIMO雷达中波达角和多普勒频率联合估计问题的克拉美罗界（Cramer-Rao bound，CRB）。最后提供了详尽的仿真实验以验证算法的性能。
The problem of joint direction of arrival (DOA) and Doppler frequency estimation of coherent targets in a monostatic multiple-input multiple-output (MIMO) radar is addressed. Based on the propagator method (PM), an RD-FSS-PM algorithm is proposed, which can effectively estimate the DOA and Doppler frequency of coherent targets with low computational load. In the RD-FSS-PM algorithm, we firstly perform a reduced-dimension (RD) transformation on received signals to decrease the computational load, then use forward spatial smoothing (FSS) to decorrelate the coherent signals and apply the PM to estimate the DOA and Doppler frequency simultaneously, which are automatically paired. Compared with the conventional FSS-PM method, the RD-FSS-PM algorithm has much better DOA estimation performance, very close frequency estimation accuracy and much less complexity. The variance of the estimation error and the Cramer-Rao bound (CRB) of the DOA and frequency estimation are derived. Simulation results are presented to show the effectiveness and improvement of the new approach.