基于压缩感知的空时自适应动目标参数估计

该文针对空时自适应处理(Space-Time Adaptive Processing, STAP)中目标参数估计问题，提出一种基于压缩感知(Compressed Sensing, CS)技术的估计方法，该方法根据目标信号在空时域的稀疏特性，利用CS技术实现目标信号重构从而估计出目标参数。为了解决稀疏恢复有效性与参数估计精度之间的矛盾，该文构造较小维数的基字典以确保基字典中各原子向量之间相关性尽可能小，并将此时得到的目标参数作为粗估值；接着在以粗估结果为邻域的区间内进行局部寻优，得到精确的估计结果。仿真结果证实了所提方法的有效性。

Space Time Adaptive Parameter Estimation of Moving Target Based on Compressed Sensing

In this paper, by exploiting the intrinsic sparsity of the moving target in the angle-Doppler domain, a new space time adaptive moving target parameter estimation algorithm is proposed, which uses the technique of sparse recovery to estimate space-time parameter of the moving target. To solve the contradiction between the successful of sparse recovery probability and the higher resolution, a small dictionary is selected to keep the coherence value between every two adjacent columns of the dictionary equal to minimize, and the parameter estimated from the above sparse recovery is regard as a rough result. To obtain a more precise result, a following match filter is applied to the local neighborhood of the obtained rough value. Effectiveness of the new method is verified via simulation examples.