基于Bayesian方法的鲁棒约束LMS算法

输入信号的方向向量出现偏差时,最小均方误差算法会出现收敛速度慢、输出性能下降、不稳定等问题.本文针对这些问题,对传统LMS(least mean squares)算法进行了改进,提出了基于Bayesian方法的鲁棒约束LMS算法.该算法利用信号的先验信息对实际信号方向向量进行估计,有效地抑制了方向向量偏差的影响,并提高了系统的鲁棒性.阵列输出的信干噪比得到了改善,更加接近最优值.仿真实验验证了该算法的有效性和可行性.

A Bayesian Approach to Robust Constrained-LMS Algorithm

In the presence of signal steering vector mismatches,least mean squares(LMS) algorithm displays such problems as low convergence speed,degraded output performance and instability.In order to overcome the shortages and to improve the traditional LMS algorithm,this paper presents a robust constrained-LMS algorithm based on Bayesian approach.By using prior knowledge,the proposed algorithm can estimate the actual signal steering vector,thus effectively reduces the influence of signal steering vector mismatches and improves the system robustness.The mean output array signal-to-interference-plus-noise ratio(SINR) is improved,and is closer to the optimal value.Simulation results are given to demonstrate the effectiveness and feasibility of the proposed algorithm.