离网误差迭代自校正STAP算法

基于稀疏恢复技术的空时自适应处理（Sparse Recovery Space-Time Adaptive Processing，SR-STAP）方法提升了动目标检测性能。然而，当出现离网效应时，SR-STAP算法的杂波抑制性能下降。为了解决离网效应问题，本文提出了一种离网误差迭代自校正STAP算法。该算法首先从常规全局STAP字典中选取了功率谱值较高的网格点来构建大字典。其次，从大字典中找到与杂波点匹配度较高的原子为中心，构建局部STAP字典；接着，利用贝叶斯后验概率最大思想进行局部搜索，找到与杂波点匹配度最高的网格点。最后，得到了经过修正后的STAP字典和最优STAP滤波权值，优化了离网情况下STAP算法性能。通过仿真验证了算法消除离网误差的有效性。 

STAP Algorithm Based on Iterative Self- calibrated method for Off-Grid

Space recovery space-time adaptive processing method based on sparse recovery (SR-STAP) improves the performance of moving target detection. However, when the off-grid effect occurs, the performance of SR-STAP algorithm was degraded. Hence, an off-grid error iterative self-calibrated STAP algorithm was proposed to solve this problem. Firstly, the grid points with high power spectrum value were selected from the general global dictionary to construct the large dictionary. Secondly, the atoms with high matching degree with clutter points were found from the large dictionary as the center, and the local dictionary was constructed. In addition, the local search was carried out by considering the posterior Bayesian, and the highest matching degree with clutter points was founded. Finally, the calibrated dictionary and the optimal STAP filter weight are obtained, which optimizes the performance of STAP algorithm in off-grid situation. The effectiveness of the algorithm is verified by simulations.