A modified PCA neural network to blind estimation of the PN sequence in lower SNR DS-SS signals

A modified principal component analysis (PCA) neural network (NN) based on signal eigen-analysis is proposed to blind estimation of the pseudo noise (PN) sequence in lower signal to noise ratios (SNR) direct sequence spread spectrum (DS-SS) signals. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is two periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors. The PN sequence can be estimated by the principal eigenvector of autocorrelation matrix in the end. Since the duration of temporal window is two periods of PN sequence, the PN sequence can be reconstructed by the first principal eigenvector only. Additionally, the eigen-analysis method becomes inefficiency when the estimated PN sequence becomes longer. We can use a PCA NN to realize the PN sequence estimation from lower SNR input DS-SS signals effectively.