Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal
For the multisensor multi-channel autoregressive moving average (ARMA) signal with white measurement noises and a common disturbance measurement white noise, when the model parameters and the noise variances are all unknown, a multi-stage information fusion identification method is presented, where...
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| Vydáno v: | Signal processing Ročník 91; číslo 8; s. 2028 - 2041 |
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| Médium: | Journal Article |
| Jazyk: | angličtina |
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Amsterdam
Elsevier B.V
01.08.2011
Elsevier |
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| ISSN: | 0165-1684, 1872-7557 |
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| Abstract | For the multisensor multi-channel autoregressive moving average (ARMA) signal with white measurement noises and a common disturbance measurement white noise, when the model parameters and the noise variances are all unknown, a multi-stage information fusion identification method is presented, where the consistent fused estimates of the model parameters and noise variances are obtained by the multi-dimension recursive instrumental variable (RIV) algorithm, correlation method and Gevers–Wouters algorithm with a dead band. Substituting these estimates into the optimal distributed measurement fusion Kalman signal estimator, a self-tuning distributed measurement fusion Kalman signal estimator is presented. Its convergence is proved by the dynamic error system analysis (DESA) method, so that it has asymptotical global optimality. In order to reduce computational load, a fast recursive inversion algorithm for a high-dimension matrix is presented by the inversion formula of partitioned matrix. Especially, when the process and measurement noise variance matrices are all diagonal matrices, the inversion formula of a high-dimension matrix is presented, which extends the formula of the inverse of Pei–Radman matrix. Applying the proposed inversion algorithm, the computation of the fused measurement and fused noise variance is simplified and their computational burden is reduced. A simulation example shows effectiveness of the proposed method. |
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| AbstractList | For the multisensor multi-channel autoregressive moving average (ARMA) signal with white measurement noises and a common disturbance measurement white noise, when the model parameters and the noise variances are all unknown, a multi-stage information fusion identification method is presented, where the consistent fused estimates of the model parameters and noise variances are obtained by the multi-dimension recursive instrumental variable (RIV) algorithm, correlation method and Gevers-Wouters algorithm with a dead band. Substituting these estimates into the optimal distributed measurement fusion Kalman signal estimator, a self-tuning distributed measurement fusion Kalman signal estimator is presented. Its convergence is proved by the dynamic error system analysis (DESA) method, so that it has asymptotical global optimality. In order to reduce computational load, a fast recursive inversion algorithm for a high-dimension matrix is presented by the inversion formula of partitioned matrix. Especially, when the process and measurement noise variance matrices are all diagonal matrices, the inversion formula of a high-dimension matrix is presented, which extends the formula of the inverse of Pei-Radman matrix. Applying the proposed inversion algorithm, the computation of the fused measurement and fused noise variance is simplified and their computational burden is reduced. A simulation example shows effectiveness of the proposed method. |
| Author | Deng, Zili Ran, Chenjian |
| Author_xml | – sequence: 1 givenname: Chenjian surname: Ran fullname: Ran, Chenjian – sequence: 2 givenname: Zili surname: Deng fullname: Deng, Zili email: dzl@hlju.edu.cn |
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| Cites_doi | 10.1109/ICCA.2010.5524102 10.1109/7.7186 10.1109/WCICA.2008.4593476 10.1109/WCICA.2010.5554765 10.1109/7.104265 10.1016/j.automatica.2007.07.008 10.1016/0005-1098(96)85549-X 10.1109/JSEN.2009.2033260 10.1007/s11768-005-0011-8 10.1002/acs.1178 10.1109/CCDC.2009.5191948 10.1049/ip-vis:20041260 10.1016/j.sigpro.2008.10.019 10.1016/j.automatica.2004.01.014 10.1016/j.automatica.2007.01.010 10.1109/ICCA.2010.5524182 10.1109/ICCA.2010.5524057 10.1016/j.automatica.2005.04.020 10.1109/7.913685 10.1109/TAES.2009.4805272 10.1016/j.automatica.2004.03.012 10.1109/WCICA.2010.5554235 10.1109/TIT.1977.1055719 10.1109/TAC.1984.1103464 |
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| Keywords | Fast inversion algorithm Asymptotical global optimality Distributed measurement fusion Self-tuning fused signal estimator Multisensor information fusion Multi-channel ARMA signal Convergence Performance evaluation ARMA model Information integration Error estimation Sensor fusion Dynamical system Correlation method White noise Fast algorithm Multistage method Multisensor Recursive algorithm Computational complexity Inverse problem Simulation Matrix inversion Information processing Recursive method Signal processing Data fusion |
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| SubjectTerms | Algorithms Applied sciences Asymptotical global optimality Computation Convergence Detection, estimation, filtering, equalization, prediction Distributed measurement fusion Estimates Estimators Exact sciences and technology Fast inversion algorithm Information theory Information, signal and communications theory Inversions Mathematical models Matrices Multi-channel ARMA signal Multisensor information fusion Noise Self-tuning fused signal estimator Signal and communications theory Signal, noise Telecommunications and information theory |
| Title | Self-tuning distributed measurement fusion Kalman estimator for the multi-channel ARMA signal |
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