Improved Direct Sequence Spread Spectrum Signal PN Estimation using Maximum Likelihood Algorithm

This study presents a Pseudo noise sequence (PN) estimation algorithm using maximum likelihood method in low signal to noise ratio. The received signal samples are divided into temporal segments. Then correlation matrix is computed for eigenvalue estimation. Eigenvector related to largest eigenvalue of this matrix is chosen and de-noised by stationary wavelet transform to find asynchronous of sequence and chip rate. The estimation of PN sequence, is found through a maximum likelihood algorithm for delay estimation and interpolation filter. Simulation results are applied to evaluate the proposed method and compare with previous methods in terms of computational complexity and accuracy of the chip rate and the PN estimation. Furthermore, minimum number of required samples are investigated for true estimation accuracy measurement. The results indicated that, the proposed method presented 13% better accuracy of PN sequence estimation compared to other methods.