Abstract:

When using the eigenvalue decomposition algorithm, the singular value decomposition (SVD) algorithm and projection approximation subspace tracking with the deflation (PASTd) algorithm to estimate pseudo-noise (PN) sequence in the estimation of PN sequence of direct sequence spread spectrum signals, there is a problem that the maximum eigenvector will be disturbed when the largest eigenvalue and the next largest eigenvalue are close, which will affect the estimation of the spread spectrum sequence. To solve this problem, a spreading sequence estimation algorithm based on orthogonal characteristic is proposed. Firstly, the received signal is divided into data segments with the length of twice the symbol width and 50% of the data overlapping after the chip rate of PN sequence and the PN sequence period are estimated. Then the largest eigenvector and the next largest eigenvector are estimated by SVD. Finally, the spreading sequence can be estimated with both orthogonal characteristics. The algorithm can estimate spreading sequence without the knowledge of desynchronization time and the simulation results show that the algorithm has the advantages of high stability, less data required and good estimation performance at a low signal-to-noise ratio.