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Lattice filter based autoregressive spectrum estimation with joint model order and estimation bandwidth adaptation
Abstract
The problem of parametric, autoregressive model based estimation of a time-varying spectral density function of a nonstationary process is considered. It is shown that estimation results can be considerably improved if identification of the autoregressive model is carried out using the two-sided doubly exponentially weighted lattice algorithm which combines results yielded by two one-sided lattice algorithms running forward in time and backward in time, respectively. It is also shown that the model order and the most appropriate estimation bandwidth can be efficiently selected using the suitably modified Akaike’s final prediction error criterion.
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- Copyright (2017 IEEE)
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- Category:
- Conference activity
- Type:
- materiały konferencyjne indeksowane w Web of Science
- Title of issue:
- 2017 IEEE 56th Annual Conference on Decision and Control (CDC) strony 4618 - 4625
- Language:
- English
- Publication year:
- 2017
- Bibliographic description:
- Niedźwiecki M., Meller M., Chojnacki D..: Lattice filter based autoregressive spectrum estimation with joint model order and estimation bandwidth adaptation, W: 2017 IEEE 56th Annual Conference on Decision and Control (CDC), 2017, ,.
- DOI:
- Digital Object Identifier (open in new tab) 10.1109/cdc.2017.8264341
- Verified by:
- Gdańsk University of Technology
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