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

Abstract

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.