Adaptive nulling and spatial spectral estimation using an iterated principal components decomposition
- 1 January 1991
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 3309-3312 vol.5
- https://doi.org/10.1109/icassp.1991.150161
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.<>Keywords
This publication has 4 references indexed in Scilit:
- Simple, effective computation of principal eigenvectors and their eigenvalues and application to high-resolution estimation of frequenciesIEEE Transactions on Acoustics, Speech, and Signal Processing, 1986
- Using spectral estimation techniques in adaptive processing antenna systemsIEEE Transactions on Antennas and Propagation, 1986
- Detection of signals by information theoretic criteriaIEEE Transactions on Acoustics, Speech, and Signal Processing, 1985
- Data adaptive signal estimation by singular value decomposition of a data matrixProceedings of the IEEE, 1982