一种有效色加载因子的知识辅助STAP方法

摘要

There are not adequate independent and identically distributed training samples under a heterogeneous clutter environment . Thus, this paper proposes a knowledge-aided LCMV-STAP approach. Firstly, the weight of knowledge-aided LCMV-STAP model is solved, and then the formula of the relationship between the color loading factors and the constraint constants is derived. Finally, the two color loading factors are solved. The computer simulations show that knowledge-aided LCMV-STAP has a better output signal-interference noise ratio than the traditional approach under a heterogeneous clutter environment,and the detection probability of Adaptive Matched Filter is higher than that of the traditional approach.%针对非均匀杂波环境里独立同分布训练样本较少的问题,本文提出了一种知识辅助的LCMV-STAP方法.该方法首先求解出知识辅助的LCMV-STAP模型中的权矢量表达式,然后推导出色加载因子与约束常数的关系式,最后求解出两个色加载因子.仿真结果表明,与传统STAP方法相比,知识辅助的LCMV-STAP方法在非均匀杂波环境下仍能获得较好的输出信干噪比,自适应匹配滤波器检测目标的概率优于传统STAP方法.