Statistically robust signal subspace identification
- 4 December 2002
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- No. 15206149,p. 2711-2714
- https://doi.org/10.1109/icassp.1990.116185
Abstract
The problem of signal subspace identification in the presence of transient, high-power noise or non-Gaussian noise is considered. To overcome such problems, an algorithm that results in a statistically robust singular value decomposition is proposed. This algorithm is derived from the connection between least-squares regression and the singular value decomposition. The robust singular value decomposition is then applied to the problem of estimation of the eigenstructure of a covariance matrix from raw data. The result of a Monte Carlo simulation study are presented to illustrate the effectiveness of the approach.Keywords
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