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Huber-Based Adaptive Unscented Kalman Filter with Non-Gaussian Measurement Noise

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Abstract

This paper concerns the application of Huber-based robust unscented Kalman filter (HRUKF) in nonlinear system with non-Gaussian measurement noise. The tuning factor \(\gamma \) is key factor in determining the form of Huber cost function. Traditionally, \(\gamma \) is mainly determined by experience and/or experiments. It is hard to acquire optimal parameter or achieve an optimal filtering. To solve this problem, the influence of tuning factor \(\gamma \) on the performance of HRUKF is analyzed, and then, an adaptive strategy based on projection statistics algorithm for this parameter is proposed to improve filtering performance under the conditions that the measurement noise is contaminated by heavier tails and/or outliers. Simulation results for the problem of Reentry Vehicle Tracking demonstrate the superiority of the proposed method over the traditional ones.

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Authors and Affiliations

  1. Department of Navigation Engineering, Naval University of Engineering, No.717, Liberated Boulevard, Wuhan, 430000, People’s Republic of China

    Bing Zhu, Lubin Chang, Jiangning Xu, Feng Zha & Jingshu Li

Authors
  1. Bing Zhu
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  2. Lubin Chang
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  3. Jiangning Xu
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  4. Feng Zha
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  5. Jingshu Li
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Corresponding author

Correspondence to Jiangning Xu.

Additional information

This work was supported by the National Natural Science Foundation of China Nos. 41574069, 41404002 and 61503404, the National Key Scientific Instrument and Equipment Project No. 2011YQ12004502, the Key R&D Program Nos. 2016YFB0501700 and 2016YFB0501701.

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Zhu, B., Chang, L., Xu, J. et al. Huber-Based Adaptive Unscented Kalman Filter with Non-Gaussian Measurement Noise. Circuits Syst Signal Process 37, 3842–3861 (2018). https://doi.org/10.1007/s00034-017-0736-x

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  • DOI: https://doi.org/10.1007/s00034-017-0736-x

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