Abstract
For the multi-sensor discrete linear time-invariant random system, the optimal covariance intersection (CI) fusion steady-state Kalman filters are presented, which have uncharted cross-covariance among the partial filtering errors. Their accuracies are higher than those of the partial optimal steady-state Kalman filters, and are lower than those of the optimal fusion Kalman filters which are fused by the cross-covariances. In the case that both the cross-covariance and the noise variances are uncharted, substituting the online consistent estimators of the noise variances into the optimal CI fusion Kalman filter, a self-correcting CI fusion Kalman filter is presented. I have proven its optimality asymptotically by the method of DESA (dynamic error system analysis) and the continuous properties of functions, i.e. the self-correcting CI Kalman fuser convergences to the optimal CI fuser in a implementation. One Monte-Carlo emulation example verifies the precision grade among the partial and fusion Kalman estimators, and the convergence of the self-correcting Kalman fuser.
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Acknowledgment
This work is supported by the Program for Young Teachers Scientific Research in Qiqihar University, 2014 k-M28, and funded by Scientific research innovation platform project of basic scientific research operating expenses of 2019 Heilongjiang Provincial universities (135409423) and Doctoral research Funding of Qiqihar University (340132).
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Zhang, P., Liu, J. (2021). Optimal and Self-correcting Covariance Intersection Fusion Kalman Filters. In: Wang, Y., Martinsen, K., Yu, T., Wang, K. (eds) Advanced Manufacturing and Automation X. IWAMA 2020. Lecture Notes in Electrical Engineering, vol 737. Springer, Singapore. https://doi.org/10.1007/978-981-33-6318-2_62
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DOI: https://doi.org/10.1007/978-981-33-6318-2_62
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