MIMO-STAP稀疏字典降维方法

针对机载多输入多输出(MIMO)雷达空时自适应处理(STAP)技术在使用稀疏方法恢复杂波谱时存在的计算复杂度高的问题,提出了一种字典降维方法。该方法直接使用训练样本估计出低分辨率的杂波空时谱,并在此基础上用FOCUSS算法的迭代式提升谱的分辨率,进而计算各原子的Capon功率谱并将谱值较大的原子挑出组成降维字典。当存在多普勒模糊时,则利用先验知识排除因多普勒模糊而入选的原子,将其余满足要求的原子取出组成降维字典。仿真实验表明:降维字典能够完全覆盖杂波脊线,使用降维字典在维持算法输出SINR性能的同时,可有效提升运算效率。

A Sparse Dictionary Dimension Reduction Method of MIMO-STAP

Aimed at the problem that the high computational complexity of airborne multiple input multiple output(MIMO) radar space time adaptive processing (STAP) technique exists in using the sparse method to recover the clutter spectrum, a dictionary dimension reduction method is proposed. In this method, the training samples are directly used to estimate low resolution clutter space time spectrum. On this basis, the resolution of spectrum is improved by using the iterative formula of FOCUSS algorithm. Then the Capon space time power spectrum values of each atom are calculated and the atoms with large spectral values are selected to form the reduced dimension dictionary. When there is doppler ambiguity, the prior knowledge is used to exclude the atoms selected because of doppler ambiguity, and the remaining atoms met the requirements are taken out to form the reduced dimension dictionary. The simulation results show that the reduced dimension dictionary can completely cover the clutter ridge, and simultaneously can effectively improve the operation efficiency while maintaining the SINR output performance of the algorithm.