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Self-tuning measurement fusion Kalman filter with correlated measurement noises

  • Published:
Journal of Electronics (China)

Abstract

For the multisensor system with correlated measurement noises and unknown noise statistics, 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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Authors and Affiliations

  1. Department of Automation, Heilongjiang University, Harbin, 150080, China

    Yuan Gao, Chenjian Ran & Zili Deng

Authors
  1. Yuan Gao
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  2. Chenjian Ran
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  3. Zili Deng
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Corresponding author

Correspondence to Zili Deng.

Additional information

Supported by the National Natural Science Foundation of China (No.60874063), Science and Technology Research Foundation of Heilongjiang Education Department (No.11521214), and Open Fund of Key Laboratory of Electronics Engineering, College of Heilongjiang Province (Heilongjiang University).

Communication author: Deng Zili, born in 1938, Male, Professor.

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Gao, Y., Ran, C. & Deng, Z. Self-tuning measurement fusion Kalman filter with correlated measurement noises. J. Electron.(China) 26, 614–622 (2009). https://doi.org/10.1007/s11767-008-0036-5

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  • DOI: https://doi.org/10.1007/s11767-008-0036-5

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