刘董华,,
袁帅,
王胜
重庆邮电大学信号与信息处理重庆市重点实验室 重庆 400065
基金项目:国家自然科学基金(61671095, 61371164, 61702065, 61701067, 61771085)，信号与信息处理重庆市市级重点实验室建设项目(CSTC2009CA2003)，重庆市研究生科研创新项目(CYS17219)，重庆市教育委员会科研项目(KJ1600427, KJ1600429)
详细信息
作者简介:张天骐：男，1971年生，博士后，教授，主要研究方向为通信信号的调制解调、盲处理、语音信号处理、神经网络实现以及FPGA、VLSI实现
刘董华：男，1992年生，硕士生，研究方向为通信信号盲处理
袁帅：男，1993年生，硕士生，研究方向为导航信号的捕获与跟踪
王胜：男，1994年生，硕士生，研究方向为通信信号处理
通讯作者:刘董华 993578978@qq.com
中图分类号:TN911.7计量
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被引次数:0
出版历程
收稿日期:2018-05-10
修回日期:2018-10-11
网络出版日期:2018-11-02
刊出日期:2019-04-01
Blind Estimation of the Pseudo Code Period and Combination Code Sequence for Composite Binary Offset Carrier Signal
Tianqi ZHANG,Donghua LIU,,
Shuai YUAN,
Sheng WANG
Chongqing Key Laboratory of Signal and Information Processing, Chongqing University of Posts and Telecommunications, Chongqing 400065, China
Funds:The National Natural Science Foundation of China (61671095, 61371164, 61702065, 61701067, 61771085), The Project of Key Laboratory of Signal and Information Processing of Chongqing (CSTC2009CA2003), The Chongqing Graduate Research and Innovation Project (CYS17219), The Research Project of Chongqing Educational Commission (KJ1600427, KJ1600429)
摘要
摘要:针对在非协作通信以及低信噪比下组合二进制偏移载波(CBOC)信号伪码周期和组合码序列较难估计的问题，该文提出了2次谱算法与基于径向基函数(RBF)神经网络算法。对输入信号进行2次功率谱计算，可以得到CBOC信号的伪码周期。在此基础上，首先对接收的1周期组合码序列进行重叠分段，其次优化筛选出学习系数，对每段数据向量作为RBF神经网络的输入信号并进行有监督地调节，最后对每段数据向量多次输入并反复训练权值向量就可以恢复原组合码序列。仿真结果表明，利用2次谱可以在低信噪比下估计出伪码周期；在误码率低于1%的情况下，所提出的RBF神经网络相比于反向传播(BP)神经网络与Sanger神经网络，信噪比分别提高1 dB和3 dB，并且在同等条件下所需的数据组数较少。
关键词:组合二进制偏移载波信号/
组合码序列/
2次功率谱/
伪码周期/
径向基神经网络
Abstract:For the problems of the Composite Binary Offset Carrier (CBOC) signal pseudo code period and combination code sequence are difficult to estimate in a non-cooperative context, two blind methods are proposed based on power spectrum reprocessing and Radial Basis Function (RBF) neural networks. It can get the CBOC pseudo code period through two power spectrum calculations. Firstly, the received one pseudo code period is overlapped segmentation based on the estimated pseudo code period. Secondly, the learning coefficient is optimized selection and each segment of date vector as an input signal to the RBF neural networks to supervised adjustment. Finally, through the continuous input signal, it can restore the original combination code sequence according to the convergent weight vectors. Simulation results show that the pseudo code period can be estimated using the secondary power spectrum under low Signal-to-Noise Ratio (SNR). Compared with the Back Propagation (BP) neural networks and the Sanger neural networks, the proposed RBF neural networks improve the SNR by 1 dB and 3 dB respectively and the number of data groups required is less through RBF neural networks under the same condition.
Key words:Composite Binary Offset Carrier (CBOC) signal/
Combination code sequence/
Secondary power spectrum/
Pseudo code period/
Radial Basis Function (RBF) neural networks
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