DocumentCode :
1711990
Title :
A new Kalman filter-based recursive method for measuring and tracking time-varying spectrum of nonstationary signals
Author :
Zhang, Z.G. ; Chan, S.C. ; Chen, Xia
Author_Institution :
Dept. of Electr. & Electron. Eng., Univ. of Hong Kong, Hong Kong, China
fYear :
2013
Firstpage :
1
Lastpage :
4
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.
Keywords :
adaptive Kalman filters; autoregressive processes; recursive estimation; state estimation; KFVNM algorithm; Kalman filter algorithm-with-variable number-of-measurements; TVAR process; adaptive Kalman filter-based recursive spectrum estimator f method; arithmetic complexity reduction; fast QR decomposition algorithm; nonstationary signal time-varying spectrum measurement; nonstationary signal time-varying spectrum tracking; power quality monitoring; state estimate derivatives; time-frequency resolution; time-varying autoregressive process; Autoregressive processes; Covariance matrices; Estimation; Frequency estimation; Kalman filters; Spectral analysis; Time-frequency analysis; Kalman filter; nonstationarity; power quanlity monitoring; spectrum estimation; time-frequency analysis;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on
Conference_Location :
Tainan
Print_ISBN :
978-1-4799-0433-4
Type :
conf
DOI :
10.1109/ICICS.2013.6782838
Filename :
6782838
Link To Document :
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