卫星通信中干扰极化状态自适应跟踪算法  

Adaptive tracking algorithm of interference polarization state in satellite communication

作　　者：齐帅 郭道省 张邦宁 张晓凯 李晓光 Qi Shuai;Guo Daoxing;Zhang Bangning;Zhang Xiaokai;Li Xiaoguang(Graduate School,Army Engineering University of PLA,Nanjing 210007,China)

机构地区：[1]解放军陆军工程大学研究生院,江苏南京210007

出　　处：《信息技术与网络安全》2018年第12期48-51,57,共5页Information Technology and Network Security

摘　　要：在卫星通信中,接收信号中含有干扰极化信号时,接收端通常利用极化滤波器滤除干扰极化信号。极化滤波器能够应用的前提是需要已知干扰极化信号,而传统的算法例如最小均方(LMS)算法对干扰信号极化状态跟踪的速度和精度不够,极大地影响了极化滤波器在工程中的实用性。提出了一种基于卡尔曼滤波的自适应极化跟踪算法,并通过实验证明了在卫星通信中该算法具有收敛速度快、鲁棒性强等优点。此外,利用双极化模型解释了干扰极化状态估计的方法,建立了卡尔曼递推方程,二者可以应用于极化滤波器的设计。通过与LMS算法的比较,证明了在卫星通信中,所提出的算法在鲁棒性和收敛性方面具有更好的性能。In satellite communication,when the received signal contains interference polarization signal,the receiver usually uses polarization filter to filter the interference polarization signal.The premise that polarization filter can be used is that the known interference polarization signal is needed.The traditional algorithms,such as the least mean square(LMS)algorithm,are not accurate and fast enough to track the polarization state of the interference signal,which greatly affects the practicability of the polarization filter in engineering.We theoretically propose and experimentally demonstrate an adaptive polarization tracking scheme based on Kalman filter that has the advantages of fast convergence and strong robustness.Besides,the method of interference polarization state estimation is explained by the dualpolarization model and the Kalman recursion equations are further established,both of which are applied to the design of the polarization filter to achieve antiinterference.Moreover,we compare our proposed algorithm with the LMS algorithm,and derive that the proposed algorithm has better performance in robustness and convergence than that of the latter.

关 键 词：卡尔曼滤波 最小均方算法 极化状态 卫星通信 收敛性 鲁棒性 

分 类 号：TN918[电子电信—通信与信息系统]