Programmable blind adaptive spatial filtering

In wireless communications, including cellular communication systems, spread spectrum overlay systems, and signals intelligence applications, the degradation caused by rapidly time-varying multipath and unknown co-channel interference can be reduced by adaptive spatial filtering using antenna arrays. In this paper we propose a flexible framework for adapting a spatial filter to extract signals of interest (SOIs) while rejecting signals not of interest (SNOIs) without using a training signal, array calibration data, or knowledge of spatial characteristics of the desired or interfering signals. The generic method within this framework exploits user-selected properties of the SOIs and/or SNOIs to transform the received data set into a new set in which the SOIs are more highly correlated between the two sets than are the SNOIs. The framework then applies the method of canonical correlation analysis to the original and transformed data sets to obtain a linear combining weight vector that performs the spatial filtering. Simulation results illustrate the performance of algorithms developed within the new framework