一种K-means改进算法的软扩频信号伪码序列盲估计  

Blind Estimation PN Sequence in Soft Spread Spectrum Signal of Improved K-means Algorithm

作　　者：张天骐[1] 杨强[1] 宋玉龙[1] 熊梅[1] 

机构地区：[1]重庆邮电大学信号与信息处理重庆市重点实验室,重庆400065

出　　处：《电子与信息学报》2018年第1期226-234,共9页Journal of Electronics & Information Technology

基　　金：国家自然科学基金(61671095;61371164);信号与信息处理重庆市市级重点实验室建设项目(CSTC2009CA2003);重庆市教育委员会科研项目(KJ130524;KJ1600427;KJ1600429)~~

摘　　要：针对软扩频信号因采用了编码技术使得伪码序列难以估计的问题,该文提出一种基于K-means聚类改进的软扩频信号伪码序列盲估计方法。该方法首先以单倍伪码周期的窗长对接收信号进行数据分段以构造观测数据矩阵,其次利用相似测度的理论从观测数据中寻找出K-means算法最优的初始聚类中心点,然后通过搜索平均轮廓系数(Silhouette Coefficient,SC)最大的绝对值以完成伪码集合规模数的估计,最后找到估计的伪码集合规模数所对应的聚类中心点集合,进一步完成对软扩频信号伪码序列的盲估计。通过仿真实验表明,在伪码序列估计错误概率低于0.1的情况下,该文方法比未改进方法提高信噪比约4 dB;而且在同一条件下,该文方法对信号的盲解扩性能优于未改进的方法。For the problem of the soft spread spectrum signal Pseudo-Noise （PN） sequence is difficult to estimate by using the coding technology, a blind estimation PN sequence method of soft spread spectrum signal is proposed based on improved K-means algorithm. Firstly, the received signal is divided into continuous non-overlapping temporal vectors according to one period of PN sequence to construct observation data matrix. Secondly, the similarity measure theory is applied to find out the optimal initial clustering center point of K-means algorithm from the observed matrix. Then the number of scale of PN sequence can be estimated by searching for the maximum absolute value of the average Silhouette Coefficient （SC）. Finally, the estimated clustering center point corresponding to the number of scale of PN sequence is found, the blind estimation PN sequence of the soft spread spectrum signal is further completed. The simulation results show that the proposed method improves the Signal-to-Noise Ratio （SNR） about 4 dB compared to the traditional method under the condition of the estimation error probability of PN sequence is less than 0.1. Moreover, the blind dispreading performance is also better than unmodified method under the same condition.

关 键 词：软扩频信号 伪码序列 K-MEANS聚类 盲估计 

分 类 号：TN911.7[电子电信—通信与信息系统]