基于圆阵的联合空间极化归一化LMS

Fangfang Li, Tingting Lyu, Hao Zhang
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引用次数: 0

摘要

为了解决期望信号的到达方向与干扰信号的到达方向相同或相似时空间滤波失败的问题,首先将极化阵列矢量引入最小均方空间滤波算法(LMS),形成空间极化最小均方(SPLMS);然后,为了克服SPLMS的收敛速度与稳态误差之间的矛盾,采用空间极化归一化最小均方(SPLMS)对其进行改进。最后,对SPLMS和SPNLMS的误差曲线进行了仿真分析,发现当两种信号的DOA相同或相似时,SPLMS可以获得良好的波束形成效果。稳态误差精度明显提高。收敛速度更快、更强,优于SPLMS。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Joint Spatial-Polarization Normalized LMS Based on Circular Array
In order to solve the problem that the spatial filtering fails when the Direction of Arrival (DOA) of the desired signal and the interference signal are same or similar, first, the polarization array vector is introduced into the spatial filtering algorithm-Least Mean Square (LMS) to form the Spatial-Polarization Least Mean Square (SPLMS); then in order to overcome the contradiction between the convergence speed and steady-state error of the SPLMS, Spatial-Polarization Normalized Least Mean Square (SPLMS) is useed to improve this problem. Finally, a simulation analysis of the error curve of the SPLMS and the SPNLMS is carried out, and it is found that when the DOA of both signals are same or similar, the SPLMS can achieve a good beamforming effect. The steady-state error accuracy is obviously improved. The convergence speed is faster, stronger, and it is more superior than the SPLMS.
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