Adaptive signal-subspace processing based on first-order perturbation analysis
- 9 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- p. 120-123 vol.1
- https://doi.org/10.1109/pacrim.1991.160696
Abstract
An approach to adaptive signal-subspace processing of narrowband array data is presented. It is based on the application of first-order perturbation analysis. In the proposed approach, the correction term in the recursive estimate of the array covariance matrix at time k is viewed as a perturbation of the estimate at time k-1. Following this interpretation, the theory of perturbation of Hermitian matrices is applied in order to obtain a new recursion expressing the eigenstructure estimate of R/sub x/(k), the true array covariance matrix at time k, in terms of the eigenstructure estimate of R/sub x/(k-1). This algorithm can be realized by means of L linear combiners with nonlinear weight-vector adaptation equations, where L is the signal-subspace dimensionality. These nonlinear adaptation equations appear to be substitutes for the orthonormal weight constraints found in other algorithms. The results of preliminary simulations are discussed.Keywords
This publication has 5 references indexed in Scilit:
- Determining the number of signals by information theoretic criteriaPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Efficient, numerically stabilized rank-one eigenstructure updating (signal processing)IEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
- Array Signal ProcessingPublished by Springer Nature ,1989
- Adaptive eigensubspace algorithms for direction or frequency estimation and trackingIEEE Transactions on Acoustics, Speech, and Signal Processing, 1988
- Coherent signal-subspace processing for the detection and estimation of angles of arrival of multiple wide-band sourcesIEEE Transactions on Acoustics, Speech, and Signal Processing, 1985