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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Bibliographic Details
Published in:Signal processing Vol. 91; no. 8; pp. 2028 - 2041
Main Authors: Ran, Chenjian, Deng, Zili
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 01.08.2011
Elsevier
Subjects:
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
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
ISSN:0165-1684, 1872-7557
Online Access:Get full text
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Summary: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.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
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content type line 23
ISSN:0165-1684
1872-7557
DOI:10.1016/j.sigpro.2011.03.010