A generalised algorithm for polarity correlation estimation using binary patterns

Abstract

A recently developed binary-pattern correlator uses the binary patterns of a group of successive samples of the polarity-sampled signal for correlation estimation. At high sampling rates (compared to the bandwidth of the signal), only a limited number of polarity changes may take place within a finite signal duration. Thus, a finite interval of a polarity-sampled signal may only have a limited set of binary patterns. The correlator estimates the frequency of occurrence of these patterns on-line and stores them in tables. The actual correlation coefficients are then deduced from these tables. Because the number of patterns is limited, efficient operation results. A general binary-pattern correlation algorithm is presented, with examples implemented on a Z-80 microprocessor. These examples represent different implementations of the algorithm. They differ in the number of signal samples effectively input each time, and in the size of the frequency of pattern-occurrence tables. As a result they have different sampling rates, and different off-line processing intervals. However, there is a trade-off between the achieved sampling rate and the amount of off-line processing.