Blind estimation of spread spectrum code and information sequence of DSSS signals based on MCMC — UKF
Chao Ma, Limim Zhang, Jie Liu · 2017 IEEE 2nd Information Technology, Networking, Electronic and Automation Control Conference (ITNEC) · 2017
A blind estimation algorithm based on overlapping segment Markov Chain Monte Carlo-Unscented Kalman Filter(MCMC-UKF) is proposed for the problem of spread spectrum code and information sequence blind estimation of long code direct sequence spread spectrum(DSSS) signal. The algorithm is based on the Bayesian framework model, combined with the idea of overlapping segmentation, using the UKF algorithm to solve the nonlinear model, estimate the mean and variance of the posterior probability of each parameter, and finally use the MCMC method to iterate segment spread spectrum sequence, the sequence of splicing to complete the spread spectrum sequence and information sequence estimates. The algorithm can achieve effective estimation of short codes and long code signals, and is not limited by the type of spread spectrum sequences. The simulation results show that the proposed algorithm has better performance with low SNR.