基于先验知识及其定量评估的自适应杂波抑制研究

 为了改善训练样本数受限的非均匀杂波环境中的系统检测性能,研究了基于先验知识及其定量评估的自适应杂波抑制算法。提出了使用经真实杂波信息白化后的先验杂波协方差矩阵与单位矩阵之差的谱范数,来定量评估杂波先验知识的准确程度,并给出了真实杂波协方差矩阵未知时的矩阵谱范数估计方法。结合先验知识定量评估结果,获得了具有先验知识约束时的杂波协方差矩阵最大似然估计方法。分别基于多脉冲相参雷达以及空时自适应雷达进行了杂波建模,在此基础之上分析了算法性能。仿真结果证实了该算法优于使用样本协方差矩阵及先验杂波信息形成杂波抑制权值的性能。

Adaptive Clutter Suppression Research Based on Priori Knowledge and Its Accuracy Evaluation

To overcome the limitation on the number of homogeneous samples in heterogeneous clutter environments, an algorithm is proposed for suppressing clutter based on a priori knowledge and its accuracy evaluation. First, the spectral norm of the priori clutter covariance matrix, which is whitened by the true clutter covariance matrix and then subtracted by an identity matrix, is computed as a measure for the accuracy of the priori knowledge. Given that the true clutter covariance matrix is unknown, a method for adaptively estimating the spectral norm is proposed. Then with the evaluation result of the accuracy of the priori knowledge, a maximum likelihood estimation of the true clutter covariance matrix is obtained under a knowledge constraint. Finally, after constructing the clutter model of the coherent-pulse radar and space time adaptive radar respectively, the proposed algorithm is analyzed through numerical simulation. Simulation results show that the performance of the proposed algorithm is superior to the method in which the adaptive weight is formulated either by the sample covariance matrix or the priori covariance matrix.