Fast cross-correlation mitigation via minimum mean-square error estimation based on matched filter outputs for consecutive DSSS signals

Abstract

In the direct sequence spread spectrum (DSSS) system, multiple-access interference mitigation is critical to improving the acquisition performance. Therefore, in this paper, a fast cross-correlation mitigation algorithm based on the minimum mean-square error criterion is proposed. The algorithm exploits the matched filter outputs; hence, it is applicable to existing DSSS receivers based on direct pseudorandom noise code correlation. Essentially, the proposed scheme is an adaptive filter that optimizes each individual uncertain code phase delay. The algorithm employs both the code sequence information of the desired signal and that of the multiple-access interference signal, which enables it to outperform the standard matched filter, conventional least-mean-squares algorithm, and the pulse compression repair method. Furthermore, the proposed algorithm decreases the computational load compared with the existing multistatic adaptive pulse compression method by reducing the stage-number of the adaptive filter; this results in a slight degradation in performance. Numerical results verify the validity of the proposed algorithm.