A Simple Neural Cross-Correlation Engine
Jonathan C. Tapson, Ralph Etienne‐Cummings · 2007
The paper presents an electronic circuit that produces a cross-correlation function for pairs of input signals, without explicitly performing a mathematical algorithm. The circuit uses noisy integrate-and-fire neurons to produce autocorrelation information. Cross-correlation terms are generated by summing a reference signal with the incoming unknown signal. The authors show that the system is capable of extracting the cross-correlation components, and therefore the range information, in the pseudorandom gold codes used to provide ranging signals in the global positioning system (GPS). The circuit is constructed using low power discrete analog electronics, and hence offers the potential for a reduction in GPS receiver power consumption.