Abstract
In practical applications, Kalman filter and its variants such as UKF may suffer from the time-varying measurement noise and process-error. Especially, when the process-error is heavy-tailed probability distribution, the Gaussian assumption would be no longer as accurately as expected. Aiming at the time-varying measurement noise and the situation with heavy-tailed process-error problems, in this paper, a new algorithm is proposed based on Student’s t-distribution and multi-sensor information fusion. The robustness of the proposed algorithm is guaranteed by the timely estimation of the measurement noise, and the adaptiveness is realized by replacing the Gaussian by the Student’s t-distribution. The Kullback-Leible Divergence (KLD) is used as the criterion for distinguishing the Gaussian distribution from the Student’s t-distribution. Finally, a challenging target tracking example is presented and the simulation results show that the proposed algorithm achieves a higher accuracy than the other algorithms.
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Acknowledgements
This work is supported by the National Key Research and Development Program of China (2017YFC0821102, 2016YFB0502004).
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Wang, D., Zhang, H., Huang, H. (2022). An Adaptive Robust Student’s t-Based Kalman Filter Based on Multi-sensor Fusion. In: Liu, H., et al. Intelligent Robotics and Applications. ICIRA 2022. Lecture Notes in Computer Science(), vol 13456. Springer, Cham. https://doi.org/10.1007/978-3-031-13822-5_54
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DOI: https://doi.org/10.1007/978-3-031-13822-5_54
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