全数字接收机中的快速定时恢复问题

doi:10.13190/jbupt.200605.107.172

北京邮电大学学报 ›› 2006, Vol. 29 ›› Issue (5): 107-110.doi: 10.13190/jbupt.200605.107.172

全数字接收机中的快速定时恢复问题

王娜1, 单超2, 郭道省2, 张邦宁2

1. 解放军理工大学理学院, 南京 211101; 2. 解放军理工大学通信工程学院, 南京 210007

收稿日期:2005-07-20 修回日期:1900-01-01 出版日期:2006-10-30 发布日期:2006-10-30
通讯作者: 王娜

Fast Symbol Timing Recovery in All Digital Receiver

WANG Na1, SHAN Chao2, GUO Dao-sheng2, ZHANG Bang-ning2

1. Institute of Sciences, People’s Liberation Army University of Science and Technology, Nanjing 211101, China;
2. Institute of Communication Engineering, People’s Liberation Army University of Science and Technology, Nanjing 210007, China

Received:2005-07-20 Revised:1900-01-01 Online:2006-10-30 Published:2006-10-30
Contact: WANG Na

摘要/Abstract

摘要：

平方法的性能受观测时间长度的制约，并且考虑频偏时，观测时间长度对算法性能的影响变得更为复杂。针对这一特点，研究了加入卡尔曼滤波后频偏及观测时间长度对定时系统性能的影响。对二阶卡尔曼滤波的结构设计及参数设定进行了分析讨论。仿真结果表明，加入二阶卡尔曼滤波后，在较短的观测时间长度下，定时误差估计精度明显提高，从而有效地解决了平方法在计算精度和速度之间的矛盾。综合考虑算法估值精度、处理速度和计算复杂度等多方面因素，二阶卡尔曼优于一阶卡尔曼，更能适应全数字接收机快速实现定时恢复的要求。

关键词: 平方定时估计算法, 观测时间长度, 卡尔曼滤波, 频偏

Abstract:

The performance of squaring algorithm is restricted by observation interval. Furthermore, the influence becomes more complex when the frequency offset is considered. Due to above problems, the Kalman filter was introduced. Then influences of frequency offset and observation interval on symbol timing recovery system under this circumstance were studied. The design of configuration and parameter for the second-order Kalman filter was analyzed and discussed. Simulation results show that the estimation accuracy is observably improved by joining the second-order Kalman filter when the observation interval is short. Consequently, the contradiction between the calculating precision and speed is effectively solved. Comprehensively considering precision, processing speed and computation complexity, the second-order Kalman filter is better than the first-order. It is more suitable for an all-digital symbol timing recovery system.

Key words: squaring algorithm, observation interval, Kalman filtering, frequency offset

中图分类号:

TN911.72

王娜1, 单超2, 郭道省2, 张邦宁2. 全数字接收机中的快速定时恢复问题[J]. 北京邮电大学学报, 2006, 29(5): 107-110.

WANG Na1, SHAN Chao2, GUO Dao-sheng2, ZHANG Bang-ning2. Fast Symbol Timing Recovery in All Digital Receiver[J]. JOURNAL OF BEIJING UNIVERSITY OF POSTS AND TELECOM, 2006, 29(5): 107-110.

参考文献

[1] Ning Ma, Bouchard M, Goubran R A. Speech enhancement using a masking threshold constrained Kalman filter and its heuristic implementations. IEEE Transactions on Audio, Speech and Language Processing, 2006, 14(1): 19-32.
[2] St-Pierre M, Gingras D. Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system//2004 IEEE Intelligent Vehicles Symposium. Parma: IEEE, 2004: 831-835.
[3] Paul A S, Wan E A. Dual Kalman filters for autonomous terrain aided navigation in unknown environments//2005 IEEE International Joint Conference on Neural Networks. Montreal: IEEE, 2005: 2784-2789.
[4] Nordsjo A E. Target tracking based on Kalman-type filters combined with recursive estimation of model disturbances//2005 IEEE International Radar Conference. Virginia: IEEE, 2005: 115-120.
[5] McGuire M, Sima M. Low-order Kalman filters for channel estimation //IEEE Pacific Rim Conference on Communications, Computers and Signal Processing. Victoria: IEEE, 2005: 352-355.
[6] Oerder M, Meyr H. Digital filter and square timing recovery[J]. IEEE Trans Commun, 1988, 36(5): 605-611.
[7] Wang Yan, Serpedin Erchin, Ciblat Philippe. Blind feedforward cyclostationarity-based timing estimation for linear modulations[J]. IEEE Transactions on Wireless Communication, 2004, 3(3): 709-715.
[8] Kay Steven M. 统计信号处理基础——估计与检测理论[M]. 北京: 电子工业出版社, 2003. 343-352. Kay Steven M. Fundamentals of statistical signal processing-estimation and detection theory[M]. Beijing: Publishing House of Electronics Industry, 2003. 343-352.
[9] 王晓湘. 多普勒频率跟踪的四阶加权扩展卡尔曼滤波器[J]. 北京邮电大学学报, 2001, 24(1): 88-91. Wang Xiaoxiang. An adaptive four dimensional Kalman filter for doppler frequency tracking[J]. Journal of Beijing University of Posts and Telecommunications, 2001, 24(1): 88-91.
[10] 郑大春, 项海格. 卡尔曼滤波在相位估计中的应用[J]. 电子测量技术, 1997(1): 1-5. Zheng Dachun, Xiang Haige. The applications of Kalman filtering in phase estimation[J]. Electronic Measurement Technology, 1997(1): 1-5.

[1]	刘金铭张玉艳张碧玲. 基于LSTM-KF的无人机航迹跟踪算法[J]. 北京邮电大学学报, 2022, 45(5): 121-128.
[2]	梁玉珠, 沈雪微, 邱磊, 陈柏生, 王田. 物联网中基于扩展卡尔曼滤波的移动群体定位[J]. 北京邮电大学学报, 2019, 42(2): 95-100.
[3]	张羽, 李更祥, 张鹏飞, 韦琳哲, 刘纪元. 基于多传感器数据融合的合成孔径声纳运动补偿算法[J]. 北京邮电大学学报, 2017, 40(5): 82-86.
[4]	冯晟, 吴成东, 张云洲. 基于改进APIT的移动机器人动态定位[J]. 北京邮电大学学报, 2016, 39(5): 67-71,88.
[5]	黄艳艳, 彭华. 分布式MIMO-OFDM系统多频偏盲估计[J]. 北京邮电大学学报, 2015, 38(6): 6-10.
[6]	何异舟, 崔高峰, 李鹏旭, 王程, 王卫东. 卫星移动终端增强型卡尔曼滤波协同自主定位算法[J]. 北京邮电大学学报, 2015, 38(3): 94-98.
[7]	尹露, 邓中亮, 席岳. 基于可信度的多源定位数据融合方法[J]. 北京邮电大学学报, 2014, 37(4): 34-38.
[8]	门爱东, 姜竹青, 王宇鹏, 黄承恺. 双重自适应UKF在SINS初始对准中的应用[J]. 北京邮电大学学报, 2014, 37(1): 11-15.
[9]	卢继华, 刘丹, 郑建勇, 刘子群, 王爱华. MIMO-OFDM系统下新型联合载波频偏估计方法[J]. 北京邮电大学学报, 2013, 36(6): 1-6.
[10]	邓中亮, 尹露, 杨磊, 余彦培, 席岳. 基于联邦卡尔曼的GPS/基站定位信息融合算法[J]. 北京邮电大学学报, 2013, 36(6): 32-36.
[11]	朱明强, 侯建军, 刘颖, 苏军峰. 基于尺度优化IUKF滤波的室内定位估计方法[J]. 北京邮电大学学报, 2013, 36(4): 65-70.
[12]	姜政, 李中年, 张欣, 杨大成. 上行MIMO-OFDMA系统的频偏和信道联合估计算法[J]. 北京邮电大学学报, 2008, 31(3): 76-79.
[13]	赵琳, 刘剑飞, 于晓然, 王现彬. CDMA系统中改进的功率控制算法研究[J]. 北京邮电大学学报, 2007, 30(3): 96-99.
[14]	刘雨，望育梅，张惠民. 一种无线传感器网络中的信息驱动节点选择机制[J]. 北京邮电大学学报, 2006, 29(6): 62-66.
[15]	郑来波，胡健栋. 基于信号重构的OFDM频偏跟踪算法的跟踪范围[J]. 北京邮电大学学报, 2005, 28(2): 80-83.

全数字接收机中的快速定时恢复问题

Fast Symbol Timing Recovery in All Digital Receiver

PDF

可视化

摘要/Abstract

引用本文

使用本文

参考文献

相关文章 15

编辑推荐

Metrics

本文评价