Systolic array architecture for adaptive eigenstructure decomposition of correlation matrices

Eigenstructure decomposition of correlation matrices is an important pre-processing stage in many modern signal processing applications. In an unknown and possibly changing environment, adaptive algorithms that are efficient and numerically stable as well as readily implementable in hardware for eigendecomposition are highly desirable. Most modern real-time signal processing applications involve processing large amounts of input data and require high throughput rates in order to fulfil the needs of tracking and updating. The authors consider the use of a novel systolic array architecture for the high throughput online implementation of the adaptive simultaneous iteration method (SIM) algorithm for the estimation of the p largest eigenvalues and associated eigenvectors of quasi-stationary or slowly varying correlation matrices