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Circuits, Systems, and Signal Processing

, Volume 37, Issue 9, pp 3842–3861 | Cite as

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

  • Bing Zhu
  • Lubin Chang
  • Jiangning Xu
  • Feng Zha
  • Jingshu Li
Article

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.

Keywords

Nonlinear system Robust Unscented Kalman filter Projection statistics Adaptive Tuning factor 

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  • Bing Zhu
    • 1
  • Lubin Chang
    • 1
  • Jiangning Xu
    • 1
  • Feng Zha
    • 1
  • Jingshu Li
    • 1
  1. 1.Department of Navigation EngineeringNaval University of EngineeringWuhanPeople’s Republic of China

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