基于降秩多级维纳滤波器的零陷加宽算法

A novel null broadening algorithm based on reduced-rank multistage wiener filter

自适应阵列处理中,常规的零陷加宽技术在自适应权和数据失配时具有较强的鲁棒性,但该技术同时会带来计算量的增加。通过推导一种新的相关相减结构多级维纳滤算法(CSA-MWF)的向前递推形式,进而将零陷加宽技术引入降秩CSA-MWF中,提出了基于降秩CSA-MWF的零陷加宽算法。相比于常规的零陷加宽技术,该方法有效地降低了计算量。计算机仿真表明,小快拍条件下新算法的干扰抑制性能优于常规零陷加宽法。

Though the traditional null broadening algorithm is robust when the adaptive weight mismatches the data of measurement in adaptive array processing, the computational burden increases meanwhile. A novel forward recursion algorithm is deduced, and the null broadening algorithm based on reduced-rank CSA-MWF is proposed by applying the null broadening technique to the novel algorithm. Compared with the traditional null broadening technique, the proposed approach reduces the computation complexity obviously. Simulation results demonstrate its superiority to null broadening technique in low sample support environments.