北京邮电大学学报

  • EI核心期刊

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

• 研究报告 • 上一篇    下一篇

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. 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. 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:

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

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