基于降维稀疏重构的高效数据域STAP算法研究

An Efficient Data Domain STAP Algorithm Based on Reduced-Dimension Sparse Reconstruction

本文基于信号稀疏重构技术,研究利用待检测样本直接进行动目标检测的高效空时自适应处理(STAP)方案.该方案对时域降维的阵元-多普勒域数据采用空域稀疏重构技术估计高分辨率角度-多普勒谱,进而基于稀疏空时谱研究知识辅助的动目标检测算法.理论分析和仿真实验结果表明:本文算法能有效抑制杂波实现慢动目标检测,且运算量小易于实时并行处理.

An efficient direct data domain STAP scheme based on a sparse reconstruction of the primary data is presented to effectively detect ground moving targets. To reduce the computational complexity,the proposed method obtains the high resolution angle-Doppler spectrum by finding the sparsest coefficients using the reduced-dimension data in element-Doppler domain. Therefore,based on the distinct image features of clutter and targets signals,a knowledge-aided moving targets detection algorithm is also introduced. The effectiveness of the proposed approach is shown by both theoretical analysis and simulation results. This scheme is computationally efficient for real-time parallel processing.