Iterative approximation of analytic eigenvalues of a parahermitian matrix EVD
| Autor: | Ian K. Proudler, Fraser K. Coutts, Jennifer Pestana, Stephan Weiss |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
Sequence
Smoothness Maximum likelihood TK 020206 networking & telecommunications 02 engineering and technology Domain (mathematical analysis) Discrete Fourier transform Matrix (mathematics) 0202 electrical engineering electronic engineering information engineering Iterative approximation Applied mathematics 020201 artificial intelligence & image processing Eigenvalues and eigenvectors Mathematics |
| Zdroj: | 2019 International Conference on Acoustics, Speech, and Signal Processing ICASSP |
| Popis: | We present an algorithm that extracts analytic eigenvalues from a parahermitian matrix. Operating in the discrete Fourier transform domain, an inner iteration re-establishes the lost association between bins via a maximum likelihood sequence detection driven by a smoothness criterion. An outer iteration continues until a desired accuracy for the approximation of the extracted eigenvalues has been achieved. The approach is compared to existing algorithms. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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