实测数据的降秩空时自适应处理方法

针对全空时自适应处理协方差矩阵估计和计算复杂问题,介绍了在直接处理器结构上基于主分量法和互谱法的两种降秩空时自适应处理方法,这两种方法利用杂波和干扰的低秩属性,应用采样数据来构造降维转换矩阵。对Mountain Top计划进行了详细描述,对从Mountain Top计划测得的数据应用上述两种降秩方法对其进行了分析。仿真实验表明了这两种方法的可行性,减少了计算量,并提高了有效可测收敛性。

Reduced-rank space-time adaptive processing of measured data

The limitations of the total space-time adaptive processing(STAP) are estimation of interference covariance matrix and substantial computational burden.Aimed at these problems,two reduced-rank space-time adaptive processing methods based on the direct form processes,i.e.eigen-based principal component and cross-spectral metric are introduced.These two approaches are both aimed at the low-rank nature of clutter and jamming observations,and the reduced-dimension transformation applied to the data is necessarily data dependent.The Mountain Top program is described in detail and the measured data are utilized with the Mountain Top program to conduct a comparative analysis of the two reduced-rank STAP techniques.The analysis result shows that these two approaches are effective to reduce computational burden and improve the convergence measure of effectiveness.