| 摘要: |
| 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. |
| 关键词: Multisensor weighted measurement fusion Fused parameter estimator Fused noise variance estimator Self-tuning fusion Kalman filter Asymptotic global optimality Convergence |
| DOI： |
| Received:December 26, 2008Revised:August 03, 2009 |
| 基金项目: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. |
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| Self-tuning weighted measurement fusion Kalman filter and its convergence |
| Chenjian RAN,Zili DENG |
| (Department of Automation, Heilongjiang University, Harbin Heilongjiang 150080, China) |
| 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. |
| Key words: Multisensor weighted measurement fusion Fused parameter estimator Fused noise variance estimator Self-tuning fusion Kalman filter Asymptotic global optimality Convergence |
