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A new Kalman filter-based recursive method for measuring and tracking time-varying spectrum of nonstationary signals

By X Chen, Z Zhang and SC Chan

Abstract

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 - P0302This 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.link_to_OA_fulltex

Publisher: Taiwan
Year: 2013
DOI identifier: 10.1109/icics.2013.6782838
OAI identifier: oai:hub.hku.hk:10722/189880
Provided by: HKU Scholars Hub
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