Blind Wiener filtering: estimation of a random signal in noise using little prior knowledge
- 1 January 1993
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 4, 236-239 vol.4
- https://doi.org/10.1109/icassp.1993.319638
Abstract
The authors present a method for estimating a random signal component from a data vector consisting of a piece of a narrowband random sequence corrupted with additive noise. The correlation structure of the sequence is unknown. The method is based on rank reduction principles presented by Scharf and Tufts (1987). It achieves a lower mean squared estimation error than an unbiased minimum variance estimator at the expense of introducing bias into the estimate. Its superior performance over short data records makes it useful in rapidly changing signal environments. The performance of the method is analyzed and simulations to demonstrate its effectiveness are presented.Keywords
This publication has 9 references indexed in Scilit:
- Unified performance analysis of subspace-based estimation algorithmsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- Adaptive detection using low rank approximation to a data matrixIEEE Transactions on Aerospace and Electronic Systems, 1994
- Estimation of a signal waveform from noisy data using low-rank approximation to a data matrixIEEE Transactions on Signal Processing, 1993
- Reverberation Suppression and ModelingPublished by Springer Nature ,1993
- Estimation of the signal component of a data vectorPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Reduced rank methods for solving least squares problemsPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1989
- Rank reduction for modeling stationary signalsIEEE Transactions on Acoustics, Speech, and Signal Processing, 1987
- Data adaptive signal estimation by singular value decomposition of a data matrixProceedings of the IEEE, 1982
- Estimation of frequencies of multiple sinusoids: Making linear prediction perform like maximum likelihoodProceedings of the IEEE, 1982