PROGRAMMABLE CANONICAL CORRELATION-ANALYSIS - A FLEXIBLE FRAMEWORK FOR BLIND ADAPTIVE SPATIAL-FILTERING

We present a new framework known as the programmable canonical correla tion analysis (PCCA) for the design of blind adaptive spatial filterin g algorithms that attempt to separate one or more signals of interest from unknown cochannel interference and noise. Unlike many alternative s, PCCA does not require knowledge of the calibration data for the arr ay, directions of arrival, training signals, or spatial autocorrelatio n matrices of the the noise or interferers. A novel aspect of PCCA is the ease with which new algorithms, targeted at capturing all signals from particular classes of interest, can be developed within this fram ework. In this paper, several existing algorithms are unified within t he PCCA framework, and new algorithms are derived as examples. Analysi s for the infinite-collect case and simulation for the finite-collect case illustrate the operation of specific algorithms within the PCCA f ramework.