Fast self-tuning weighted measurement fusion Kalman filter for the ARMA signal

For the multisensor single channel autoregressive moving average(ARMA) signal with common disturbance measurement noise and sensor bias, when the model parameters, the sensor bias and the noise variances are all unknown, their consistent estimates are obtained by a multistage fused identification me...

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Veröffentlicht in:2011 IEEE International Conference on Mechatronics and Automation S. 1131 - 1136
Hauptverfasser: Chenjian Ran, Zili Deng
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.08.2011
Schlagworte:
ARMA signal
Correlation
Equations
Fast inversion algorithm
Kalman filters
Mathematical model
Multisensor data fusion
multistage identification method
Noise
Noise measurement
Self-tuning fused signal filter
Weight measurement
ISBN:9781424481132, 1424481139
ISSN:2152-7431
Online-Zugang:Volltext
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Abstract For the multisensor single channel autoregressive moving average(ARMA) signal with common disturbance measurement noise and sensor bias, when the model parameters, the sensor bias and the noise variances are all unknown, their consistent estimates are obtained by a multistage fused identification method, which includes the recursive extended least squares (RELS) algorithm, correlation method and the Gevers-Wouters algorithm with a dead band. Substituting these estimates into the optimal weighted measurement fusion(WMF) Kalman signal filter, a self-tuning WMF Kalman signal filter with asymptotic global optimality is presented. A fast inversion algorithm of the extended Pei-Radman matrix is presented in order to reduce the computational load. A simulation example verifies the effectiveness of the proposed method.
AbstractList For the multisensor single channel autoregressive moving average(ARMA) signal with common disturbance measurement noise and sensor bias, when the model parameters, the sensor bias and the noise variances are all unknown, their consistent estimates are obtained by a multistage fused identification method, which includes the recursive extended least squares (RELS) algorithm, correlation method and the Gevers-Wouters algorithm with a dead band. Substituting these estimates into the optimal weighted measurement fusion(WMF) Kalman signal filter, a self-tuning WMF Kalman signal filter with asymptotic global optimality is presented. A fast inversion algorithm of the extended Pei-Radman matrix is presented in order to reduce the computational load. A simulation example verifies the effectiveness of the proposed method.
Author Zili Deng
Chenjian Ran
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  surname: Zili Deng
  fullname: Zili Deng
  email: dzl@hlju.edu.cn
  organization: Dept. of Autom., Heilongjiang Univ., Harbin, China
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Snippet For the multisensor single channel autoregressive moving average(ARMA) signal with common disturbance measurement noise and sensor bias, when the model...
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SubjectTerms ARMA signal
Correlation
Equations
Fast inversion algorithm
Kalman filters
Mathematical model
Multisensor data fusion
multistage identification method
Noise
Noise measurement
Self-tuning fused signal filter
Weight measurement
Title Fast self-tuning weighted measurement fusion Kalman filter for the ARMA signal
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