Efficient adaptive algorithms for multichannel least squares filtering using a channel decomposition technique

Two efficient adaptive algorithms are outlined for least squares multichannel Fourier infrared filtering and system identification with the first algorithm focusing on the covariance or unwindowed data case and the second using the prewindowing data assumption for added simplification. A new channel decomposition method is used and data parameter nesting elements are resolved with proper variations for both algorithms which can carry multichannel filters with varying filter orders and are free from matrix operations.