基于新型自适应平方根容积卡尔曼滤波的RSOP均衡算法  

RSOP Equalization Algorithm Based on New Adaptive Square Root Cubature Kalman Filtering

作　　者：翁国翔 田清华[1] 王富 田凤[1] 张琦[1] 杨雷静 忻向军 Weng Guoxiang;Tian Qinghua;Wang Fu;Tian Feng;Zhang Qi;Yang Leijing;Xin Xiangjun(School of Electronic Engineering,State Key Laboratory of Information Photonics and Optical Communications,Beijing Key Laboratory of Space-Ground Interconnection and Convergence,Beijing University of Posts and Telecommunication,Beijing 100876,China;School of Information and Electronics,Beijing Institute of Technology,Beijing 100081,China)

机构地区：[1]北京邮电大学电子工程学院,信息光子学与光通信国家重点实验室,天地互联与融合北京市重点实验室,北京100876 [2]北京理工大学信息与电子学院,北京100081

出　　处：《光学学报》2024年第11期23-29,共7页Acta Optica Sinica

基　　金：国家自然科学基金创新群体项目(62021005)。

摘　　要：针对相干光通信系统中,传统容积卡尔曼滤波(CKF)算法和平方根容积卡尔曼滤波(SCKF)算法对偏振态旋转(RSOP)均衡存在鲁棒性不足、泛化性弱等问题,提出了一种新型的自适应SCKF算法,以实现对RSOP的跟踪补偿。该算法通过引入平方根因子直接对过程噪声协方差矩阵的平方根进行自适应更新,避免了正定分解。同时引入残差判决检测机制,对当前时刻残差值与滑窗内残差均值进行比较,将比较结果作为是否进行自适应更新的判断条件,以提升算法运行速度。仿真结果表明,所提算法在RSOP方位角变化速率为40 Mrad/s时,误码率性能满足开销为7%的前向纠错阈值条件,且相对于CKF和SCKF算法,所提算法在不同调优参量初始值下均能稳定运行。所提算法能够快速跟踪补偿RSOP,即便在初始条件不理想的情况也能稳定运行,具有鲁棒性强的优点。Objective With the rapid development of optical communication technology toward high capacity,large bandwidth,and high speed,the multi-dimensional multiplexing technology is widely researched and adopted.Polarization multiplexing technology is an important multiplexing technique.However,polarization introduces damage to polarization multiplexing systems.In extreme weather conditions such as lightning near optical cables,the Kerr effect,and the Faraday effect,rapid rotation of the polarization state of the signal can be caused.This rotation disrupts the orthogonality of the two polarization states,thus increasing the bit error rate.Therefore,it is significant to trace and compensate for polarization state rotation.Currently,equalization algorithms for rotation of the state of polarization(RSOP)include the constant modulus algorithm(CMA),the Kalman filtering algorithm,and its derivative algorithms.The CMA is simple to implement but becomes ineffective when RSOP changes rapidly.In recent years,the focus has been realized by the Kalman filter and its derivative algorithms,including the extended Kalman filter(EKF),covariance Kalman filter(CKF),and square root covariance Kalman filter(SCKF).The EKF yields high tracking and compensation accuracy for RSOP but requires the calculation of the Jacobian determinant,which results in high algorithm complexity.The CKF avoids the computation of the Jacobian determinant,significantly reducing algorithm complexity.Although the SCKF avoids the positive definite decomposition of the state error covariance matrix in CKF,during the adaptive SCKF implementation,the process noise matrix still needs to calculate out positive definite decomposition,which cannot be fully guaranteed during the actual algorithm execution.We propose a new RSOP equalization algorithm based on adaptive square root cubature Kalman filtering.This algorithm avoids the positive definite decomposition of Q and exhibits adaptive updating of the noise covariance matrix in various scenarios,thus enhancing the algorithm

关 键 词：光通信 平方根容积卡尔曼滤波算法 偏振复用 偏振态旋转 残差判决 

分 类 号：TN913.7[电子电信—通信与信息系统]