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Conference Paper: A new Kalman filter-based recursive method for measuring and tracking time-varying spectrum of nonstationary signals
Title | A new Kalman filter-based recursive method for measuring and tracking time-varying spectrum of nonstationary signals |
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Authors | |
Issue Date | 2013 |
Publisher | ICICS. |
Citation | The 9th International Conference on Information, Communications and Signal Processing (ICICS 2013), Tainan, Taiwan, 10-13 December 2013. How to Cite? |
Abstract | This paper proposes a new adaptive Kalman filter-based recursive spectrum estimator for measuring time-varying spectrum of nonstationary signals. The nonstationary signal is modeled as a time-varying autoregressive (TVAR) process and the time-varying parameters are described by a smoothness priors model. A new Kalman filter algorithm with variable number of measurements (KFVNM) is employed to recursively compute the TVAR coefficients and then the time-varying spectrum. The number of measurements in the Kalman filter is determined adaptively according to the state estimate derivatives. Furthermore, a fast QR decomposition algorithm is developed to reduce the arithmetic complexity of the proposed KFVNM algorithm. Simulation results show the proposed Kalman filter-based recursive spectrum estimator can achieve a better time-frequency resolution than the conventional parametric spectrum estimations. Its potential application to power quality monitoring is also illustrated. |
Description | Session Th13 Time-frequency Analysis and System Identification - Th13.3 A New Kalman Filter-based Recursive Method for Measuring and Tracking Time-varying Spectrum of Nonstationary Signals: no. Th13.3 - P0302 |
Persistent Identifier | http://hdl.handle.net/10722/189880 |
DC Field | Value | Language |
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dc.contributor.author | Zhang, Z | en_US |
dc.contributor.author | Chan, SC | en_US |
dc.contributor.author | Chen, X | en_US |
dc.date.accessioned | 2013-09-17T15:01:04Z | - |
dc.date.available | 2013-09-17T15:01:04Z | - |
dc.date.issued | 2013 | en_US |
dc.identifier.citation | The 9th International Conference on Information, Communications and Signal Processing (ICICS 2013), Tainan, Taiwan, 10-13 December 2013. | en_US |
dc.identifier.uri | http://hdl.handle.net/10722/189880 | - |
dc.description | Session Th13 Time-frequency Analysis and System Identification - Th13.3 A New Kalman Filter-based Recursive Method for Measuring and Tracking Time-varying Spectrum of Nonstationary Signals: no. Th13.3 - P0302 | - |
dc.description.abstract | This paper proposes a new adaptive Kalman filter-based recursive spectrum estimator for measuring time-varying spectrum of nonstationary signals. The nonstationary signal is modeled as a time-varying autoregressive (TVAR) process and the time-varying parameters are described by a smoothness priors model. A new Kalman filter algorithm with variable number of measurements (KFVNM) is employed to recursively compute the TVAR coefficients and then the time-varying spectrum. The number of measurements in the Kalman filter is determined adaptively according to the state estimate derivatives. Furthermore, a fast QR decomposition algorithm is developed to reduce the arithmetic complexity of the proposed KFVNM algorithm. Simulation results show the proposed Kalman filter-based recursive spectrum estimator can achieve a better time-frequency resolution than the conventional parametric spectrum estimations. Its potential application to power quality monitoring is also illustrated. | - |
dc.language | eng | en_US |
dc.publisher | ICICS. | - |
dc.relation.ispartof | 9th ICICS 2013 | en_US |
dc.title | A new Kalman filter-based recursive method for measuring and tracking time-varying spectrum of nonstationary signals | en_US |
dc.type | Conference_Paper | en_US |
dc.identifier.email | Zhang, Z: zgzhang@eee.hku.hk | en_US |
dc.identifier.email | Chan, SC: ascchan@hkucc.hku.hk | en_US |
dc.identifier.authority | Zhang, Z=rp01565 | en_US |
dc.identifier.authority | Chan, SC=rp00094 | en_US |
dc.description.nature | link_to_OA_fulltext | - |
dc.identifier.hkuros | 223284 | en_US |
dc.publisher.place | Taiwan | - |