Time-varying frequency/spectral estimation extraction
Adaptive algorithm vs. Basis Function method
978-3-8383-4075-3
3838340752
124
2010-01-23
59.00 €
eng
https://images.our-assets.com/cover/230x230/9783838340753.jpg
https://images.our-assets.com/fullcover/230x230/9783838340753.jpg
https://images.our-assets.com/cover/2000x/9783838340753.jpg
https://images.our-assets.com/fullcover/2000x/9783838340753.jpg
A time-varying autoregressive (TVAR) approach is used for modeling nonstationary signals, and frequency information is then extracted from the TVAR parameters. Two methods may be used for estimating the TVAR parameters: the adaptive algorithm approach and the basis function approach. Adaptive algorithms, such as the least mean square (LMS) and the recursive least square (RLS), use a dynamic model for adapting the TVAR parameters and are capable of tracking time-varying frequency, provided that the variation is slow. It is observed that, if the signals have a single timefrequency component, the RLS with a fixed pole on the unit circle yields the fastest convergence. The basis function method employs an explicit model for the TVAR parameter variation, and model parameters are estimated via a block calculation. We proposed a modification to the basis function method by utilizing both forward and backward predictors for estimating the time-varying spectral density of nonstationary signals. It is shown that our approach yields better accuracy than the existing basis function approach, which uses only the forward predictor.
https://www.morebooks.de/books/ru/published_by/lap-lambert-academic-publishing/47/products
Термодинамика
https://www.morebooks.de/store/ru/book/time-varying-frequency-spectral-estimation-extraction/isbn/978-3-8383-4075-3