Adaptive nulling and spatial spectral estimation using an iterated principal components decomposition

TLDR: An iterative algorithm for computing a reduced-rank principal component least squares estimate of the adaptive weight vector to be used for spatially nulling interference received in the sidelobes of the formed beams of an array of antenna elements is described.

Abstract: An iterative algorithm for computing a reduced-rank principal component least squares estimate of the adaptive weight vector to be used for spatially nulling interference received in the sidelobes of the formed beams of an array of antenna elements is described. Based on a power/deflation method for extraction estimates of the dominant eigenstructure components, the algorithm is used to approximate the subspace spanned by the sample covariance eigenvectors associated with the directions of arrival of spatially coherent interference. Side information that can aid the discrimination between interference and noise subspaces is also produced. Performance is illustrated by the results of processing signals recorded from the individual elements of a linear antenna array operating in the HF band. >