基于正反向联合TCF的序列多项式估计算法
A method for generator polynomial estimation of PN sequence based on forward and backward combined TCF
陈松 1黄开枝1
作者信息
- 1. 国家数字交换系统工程技术研究中心,河南郑州450002
- 折叠
摘要
针对高误码条件下序列高阶多项式估计算法容错性能差、计算复杂度高等问题进行研究,提出一种基于正反向联合TCF(三阶相关函数)的序列多项式估计算法.引入多项式线性相关对,分析研究TCF估计模型.针对模型缺点,将TCF估计问题转化为序列捕获问题,采用循环相关方式求解正反向联合TCF峰值位置.运用概率分析方法,分析研究正反向TCF峰值求解中各参数关系,为算法应用提供依据.仿真结果表明,该算法在误码率37％的条件下能够较好地完成17阶m序列多项式估计,且性能不受制于多项式抽头个数,计算复杂度和容错性能均优于TCF算法.
Abstract
The issue of poor error tolerance and heavy computation of current high-order polynomial estimation methods under error conditions is studied.A novel algorithm which estimates the generator polynomial of PN sequence using forward and backward joint TCF(triple correlation function) is proposed.By the tool of sequential linear correlation pair,the estimation model of TCF is derived and analyzed.Analysis indicates that the TCF estimation can be equivalent to sequential acquisition,according to which the forward-backward jointed TCF peak is obtained by cyclic correlation operation to cope with the deficit of TCF model.With the statistical method,the relations and selections of those parameters of jointed TCF method are further studied,which can provide a better guidance for practical application.The simulation results show that the proposed method is irrelevant with the tapped number of generator polynomial,and has a good estimation performance for 17-order polynomials even when the sequence BER is 37％,which is better than TCF method in computation cost and error tolerance.
关键词
m序列/本原多项式/TCF算法/高阶统计/相关峰Key words
m sequence/primitive polynomial/TCF/high-order statistic/correlation peak引用本文复制引用
出版年
2013
NETL
NSTL
维普
万方数据