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基于EKF和LKF级联的频偏和相位估计联合方案 被引量:6

A Joint Frequency Offset and Phase Estimation Scheme Based on Cascaded EKF and LKF
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摘要 提出基于级联扩展卡尔曼滤波与块状处理线性卡尔曼滤波的频偏和相位噪声协同处理方案。利用扩展卡尔曼滤波器对系统频偏进行初始估计,利用块状线性卡尔曼滤波器实现频偏和相位噪声的精准估计。对最优数据块长度和调优参数Q的关系,算法的线宽容忍性能、频偏估计范围以及频偏漂移追踪速度进行了详细的讨论和分析。结果表明,该方案具有快速的载波估计收敛能力、较高的频偏和相位估计精度,并且其频偏漂移追踪可高达320 MHz/μs。相比传统盲相位搜索方法,该方案具备较高的频偏容忍度和较低的实现复杂度。最后实验研究正交相移键控(QPSK)光通信系统下的载波恢复性能,同时给出不同光信噪比、块状数据长度下的载波频偏估计性能。 A co-processing scheme of frequency offset and phase noise based on cascade extended Kalman filter(EKF)and block-processed linear Kalman filter(LKF)is proposed.The EKF is responsible for preliminary estimation of frequency offset.The LKF is responsible for tracking frequency offset and phase noise accurately.Relationship between the optimal block length and the tuning parameter Q,linewidth tolerance,frequency offset estimation range and frequency offset tracking speed of algorithm are discussed and analyzed in detail.The results show that the scheme has fast convergence performance,and can achieve high estimation accuracy of frequency offset and phase estimation.Moreover,the frequency offset drift can reach 320 MHz/μs.Compared with traditional blind phase search method,the scheme has high frequency offset tolerance and low implementation complexity.Finally,the carrier recovery performance for quadrature phase shift keying(QPSK)optical communication system is experimentally studied,and the carrier frequency offset estimation performance under different optical signal-tonoise ratios and block data lengths is given.
作者 侯冰洁 杨彦甫 向前 张群 姚勇 Hou Bingjie;Yang Yanfu;Xiang Qian;Zhang Qun;Yao Yong(Shenzhen Graduate School, Harbin Institute of Technology, Shenzhen, Guangdong 518055, China)
出处 《光学学报》 EI CAS CSCD 北大核心 2018年第1期77-82,共6页 Acta Optica Sinica
基金 国家自然科学基金(61205046 61575051) 深圳市科技计划项目(JCYJ20150327155705357 KQCX2015032409501296 JSGG20150529153336124 JCYJ20150529114045265 JSGG20170414141239041)
关键词 光通信 频偏和相位估计 频偏漂移跟踪 卡尔曼滤波 optical communications frequency offset and phase estimation frequency offset drift tracking Kalmanfilter
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