一种秩基于降秩共轭梯度法的零陷加宽技术

A Novel Null Broadening Technique Based on Reduced-Rank Conjugate Gradient Algorithm

在自适应阵列处理技术中,零陷加宽技术常用于增强自适应权和数据失配时的鲁棒性,但该技术同时会带来计算量的增加。将零陷加宽技术引入到降秩共轭梯度法中,提出了基于降秩共轭法的零陷加宽技术。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。

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 null broadening technique is proposed by applying the null broadening technique to the reduced-rank conjugate gradient 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.