| Time-Varying Frequency/Spectral Estimation Extraction Contributor(s): Steven, Hall (Author) |
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ISBN: 3838340752 ISBN-13: 9783838340753 Publisher: LAP Lambert Academic Publishing OUR PRICE: $60.53 Product Type: Paperback Published: June 2010 |
| Additional Information |
| BISAC Categories: - Science | Mechanics - Thermodynamics |
| Physical Information: 0.29" H x 6" W x 9" (0.42 lbs) 124 pages |
| Descriptions, Reviews, Etc. |
| Publisher Description: 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. |
