Estimate and Track the PN Sequence of Weak DS-SS Signals

Abstract

This paper proposes a modified Sanger's generalized Hebbian neural network method to estimate and track the pseudo noise sequence of weak direct sequence spread spectrum signals. The proposed method is based on eigen-analysis of received signals. The received signal is firstly sampled and divided into non-overlapping signal vectors according to a temporal window, which duration is a periods of PN sequence. Then an autocorrelation matrix is computed and accumulated by these signal vectors one by one. The pseudo noise sequence can be estimated and tracked by the principal eigenvector of the matrix in the end. Because the eigen-analysis method becomes inefficiency when the estimated pseudo noise sequence becomes longer or the estimated pseudo noise sequence becomes time varying, we use a modified Sanger's generalized Hebbian neural network to realize the pseudo noise sequence estimation and tracking from weak input signals adaptively and effectively.