Chip rate and pseudo‐noise sequence estimation for direct sequence spread spectrum signals
Bin Shen, Jianxin Wang · IET Signal Processing · 2017
This study presents a chip rate and pseudo‐noise (PN) sequence estimation algorithm of direct sequence spread spectrum signals. The received signal samples are divided into temporal segments, from which the correlation matrix is computed and decomposed. Then the principal eigenvector of this matrix is de‐noised. The chip rate and the PN sequence are estimated from the de‐noised principal eigenvector. The computational complexity is also evaluated. Theory analysis and computer simulation results show that, compared with other algorithms, the performance of proposed algorithm is significantly improved at the approximate computational complexity. In addition, an improved version with low computational complexity is available. Simulation results also verify the effectiveness and superior performance of improved algorithm in low signal‐to‐noise ratio.