Conference proceeding
Gauss-Newton based adaptive subspace estimation
[Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.4, pp.205-208 vol.4
1992
DOI: 10.1109/ICASSP.1992.226450
Abstract
An adaptive approach for estimating all (or some) of the orthogonal eigenvectors of the data covariance matrix (of a time series consisting of real narrowband signals in additive white noise) is presented. The inflation approach is used to estimate each of these vectors as minimum eigenvectors (eigenvectors corresponding to the minimum eigenvalue) of appropriately constructed symmetric positive definite matrices. This reformulation of the problem is made possible by the fact that the problem of estimating the minimum eigenvector of a symmetric positive definite matrix can be restated as the unconstrained minimization of an appropriately constructed nonlinear nonconvex cost function. The modular nature of the algorithm that results from this reformation makes the proposed approach highly parallel, resulting in a high-speed adaptive approach for subspace estimation.< >
Details
- Title: Subtitle
- Gauss-Newton based adaptive subspace estimation
- Creators
- G Mathew - Indian Institute of ScienceV.U Reddy - Indian Institute of ScienceS Dasgupta
- Resource Type
- Conference proceeding
- Publication Details
- [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing, Vol.4, pp.205-208 vol.4
- Publisher
- IEEE
- DOI
- 10.1109/ICASSP.1992.226450
- ISSN
- 1520-6149
- eISSN
- 2379-190X
- Language
- English
- Date published
- 1992
- Academic Unit
- Electrical and Computer Engineering
- Record Identifier
- 9984197441502771
Metrics
30 Record Views