Fast adaptive eigenvalue decomposition: a maximum likelihood approach
- 22 November 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 5, 3565-3568 vol.5
- https://doi.org/10.1109/icassp.1997.604636
Abstract
A new adaptive subspace estimation algorithm is presented, based on the maximisation of the likelihood functional. It requires little computational cost and the particular structure of the algorithm ensures the orthonormality of the estimated basis of eigenvectors. Application to moving sources localization shows the very good behavior of the algorithm when applied to problems of practical interest.Keywords
This publication has 10 references indexed in Scilit:
- A new family of EVD tracking algorithms using Givens rotationsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- An algorithm for multisource beamforming and multitarget trackingIEEE Transactions on Signal Processing, 1996
- Projection approximation subspace trackingIEEE Transactions on Signal Processing, 1995
- Subspace methods for the blind identification of multichannel FIR filtersIEEE Transactions on Signal Processing, 1995
- Convergences of adaptive block simultaneous iteration method for eigenstructure decompositionSignal Processing, 1994
- Estimation of the number of signals in the presence of unknown correlated sensor noiseIEEE Transactions on Signal Processing, 1992
- Noniterative subspace trackingIEEE Transactions on Signal Processing, 1992
- A parametric method for determining the number of signals in narrow-band direction findingIEEE Transactions on Signal Processing, 1991
- An adaptive unit norm filter with applications to signal analysis and Karhunen-Loeve transformationsIEEE Transactions on Circuits and Systems, 1990
- Multiple emitter location and signal parameter estimationIEEE Transactions on Antennas and Propagation, 1986