基于MCMC-UKF的直接序列扩频信号盲估计  

Blind Estimation of Direct Sequence Spread Spectrum Signal Based on MCMC-UKF

作　　者：马超 张立民 MA Chao;ZHANG Limin(Institute of Information Fusion,Naval Aeronautical University,Yantai,Shandong 264001,China)

机构地区：[1]海军航空大学信息融合研究所

出　　处：《计算机工程》2019年第7期95-102,共8页Computer Engineering

基　　金：国家自然科学基金重大研究计划(91538201);泰山学者专项(ts201511020)

摘　　要：针对长码直接序列扩频信号的扩频码及信息序列盲估计问题,提出基于重叠分段MCMC-UKF的扩频码及信息序列联合估计算法。在贝叶斯框架模型下,结合重叠分段的思想,利用UKF算法求解非线性模型,估计各参数后验概率的均值和方差,通过MCMC方法迭代抽样得到各分段扩频序列,进行序列拼接以完成对扩频序列及信息序列的估计。仿真结果表明,该算法能适应较低的信噪比环境,且不受扩频序列类型的限制。Aiming at the problem of spreading code and information sequence blind estimation for long code Direct Sequence Spread Spectrum(DSSS) signal,this paper proposes a joint estimation algorithm based on overlapping segment Markov Chain Monte Carlo-Unscented Kalman Filtering(MCMC-UKF) for spreading code and information sequence.Under the Bayesian framework model,combined with the idea of overlapping segmentation,the UKF algorithm is used to solve the nonlinear model,and the mean and variance of the posterior probabilities of each parameter are estimated.The segmentation spread spectrum is obtained by MCMC method,sequence splicing to complete the estimation of the spreading sequence and the information sequence.Simulation results show that the algorithm can adapt to lower Signal-to-Noise Ratio(SNR) and is not limited by the type of spread spectrum sequence.

关 键 词：直接序列扩频信号 贝叶斯模型 无迹卡尔曼滤波 分段 序列估计 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]