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Self-tuning weighted measurement fusion Kalman filter and its convergence
Received:2008/12/26  Revised:2009/8/3
Keywords:Multisensor weighted measurement fusion  Fused parameter estimator  Fused noise variance estimator  Self-tuning fusion Kalman filter  Asymptotic global optimality  Convergence
Fund Project:This work was supported by the National Natural Science Foundation of China (No.60874063), the Innovation Scientific Research Foundation for Graduate Students of Heilongjiang Province (No.YJSCX2008-018HLJ), and the Automatic Control Key Laboratory of Heilongjiang University.
AuthorInstitutionE-mail
Chenjian RAN Department of Automation, Heilongjiang University, Harbin Heilongjiang 150080, China ranchenjian@gmail.com 
Zili DENG Department of Automation, Heilongjiang University, Harbin Heilongjiang 150080, China dzl@hlju.edu.cn 
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Abstract:
      For multisensor systems, when the model parameters and the noise variances are unknown, the consistent fused estimators of the model parameters and noise variances are obtained, based on the system identification algorithm, correlation method and least squares fusion criterion. Substituting these consistent estimators into the optimal weighted measurement fusion Kalman filter, a self-tuning weighted measurement fusion Kalman filter is presented. Using the dynamic error system analysis (DESA) method, the convergence of the self-tuning weighted measurement fusion Kalman filter is proved, i.e., the self-tuning Kalman filter converges to the corresponding optimal Kalman filter in a realization. Therefore, the self-tuning weighted measurement fusion Kalman filter has asymptotic global optimality. One simulation example for a 4-sensor target tracking system verifies its effectiveness.
Chenjian RAN and Zili DENG.Self-tuning weighted measurement fusion Kalman filter and its convergence[J].Journal of Control Theory and Applications,2010,8(4):435~440.
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