空域数据分解的两级降维自适应处理方法  

Two-stage Reduced-dimension Adaptive Processing Method Based on the Spatial Data Decomposition

作　　者：周延[1] 冯大政[1] 朱国辉[1] 向平叶 

机构地区：[1]西安电子科技大学雷达信号处理国家重点实验室,西安710071

出　　处：《电子与信息学报》2015年第2期334-338,共5页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61271293)资助课题

摘　　要：传统的后多普勒自适应处理方法,如因子法和扩展因子法,虽然能大大降低自适应处理时的运算量和独立同分布样本的需求量,但在天线阵元数进一步增大的情况下,还是不能有效抑制杂波。针对这一问题,该文提出一种空域数据分解的两级降维自适应处理方法。该方法将多普勒滤波后的空域数据进行分解,使其变为两个向量的Kronecker乘积,得到一双二次代价函数,利用循环迭代的思想求解最优权。实验表明该方法具有快速收敛,所需训练样本少的优点,尤其在小样本条件下该方法抑制杂波的性能明显优于因子法和扩展因子法。The traditional post-Doppler adaptive processing approaches such as Factored Approach（FA） and Extended Factored Approach（EFA） can significantly reduce the computation-cost and training sample requirement in adaptive processing. However, their clutter suppression ability is considerably degraded with the increasing number of antenna elements. To solve this problem, a two-stage reduced-dimension adaptive processing method based on the decomposition of spatial data is proposed. This method decomposes the spatial data after Doppler filtering into a Kronecker product of two short vectors. Then a bi-quadratic cost function is obtained. The circular iteration is applied to solve the optimal weight. Experimental results show that the proposed method has the advantages of fast convergence and small training samples requirement. It has greater clutter suppression ability especially in small training samples support compared with FA and EFA.

关 键 词：雷达信号处理 空时自适应处理(STAP) 杂波抑制 降维 

分 类 号：TN959.73[电子电信—信号与信息处理]