基于迭代自适应的字典校正空时自适应处理算法

Space-time Adaptive Processing with Dictionary Calibration Based on Iterative Adaptive Approach

本文针对稀疏恢复空时自适应处理(Space-Time Adaptive Processing,STAP)由于字典设置不合适引起的离网效应,提出了一种基于迭代自适应(Iterative Adaptive Approach,IAA)的字典校正STAP算法。首先在IAA的每次迭代中,找到原始空时导向字典中每个量化空间频率最大功率对应的原子,围绕选定的原子,将其附近的多普勒频率均匀离散成一个集合,然后通过最大化联合似然函数在局域中搜索最优原子,并将选定的原子替换为最优原子,最后通过IAA的全局迭代,选择与杂波脊匹配的原子形成新的空时导向字典。实验证明,该方法可以有效地减轻离网效应引起的杂波脊扩展,杂波抑制性能优于现有的空时导向字典均匀离散化的IAA-STAP方法。

Aiming at the off-grid effect caused by improper dictionary settings due to sparse recovery space-time adaptive processing(STAP),this paper proposes a dictionary calibration STAP algorithm based on iterative adaptive approach(IAA).Firstly,in each iteration of IAA,the atom corresponding to the maximum power of each quantized spatial frequency is found in the original space-time guidance dictionary.The Doppler frequency around the selected atom is evenly discretized into a set,and then the best atom is searched by maximizing the joint likelihood function in the local area,which is used to replace the selected atom.Finally,through the global iteration of IAA,the atoms matched the clutter ridge are selected to form a new space-time steering dictionary.Experiment results show that this method can effectively reduce the clutter ridge expansion caused by the off-grid effect,and the clutter suppression performance is better than the existing IAA-STAP method with uniform discretization of the space-time steering dictionary.