Self-tuning measurement fusion Kalman filter with correlated measurement noises |
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Authors: | Yuan Gao Chenjian Ran Zili Deng |
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Affiliation: | Department of Automation,Heilongjiang University,Harbin 150080,China |
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Abstract: | For the multisensor system with correlated measurement noises and unknown noise sta-tistics, based on the solution of the matrix equations for correlation function, the on-line estimators of the noise variances and cross-covariances is obtained. Further, a self-tuning weighted measurement fusion Kalman filter is presented, based on the Riccati equation. By the Dynamic Error System Analysis (DESA) method, it rigorously proved that the presented self-tuning weighted measurement fusion Kalman filter converges to the optimal weighted measurement fusion steady-state Kalman filter in a realization or with probability one, so that it has asymptotic global optimality. A simulation example for a target tracking system with 3-sensor shows that the presented self-tuning measurement fusion Kalman fuser converges to the optimal steady-state measurement fusion Kalman fuser. |
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Keywords: | Correlation function method Multisensor measurement fusion Self-tuning Kalman filter Convergence in a realization |
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