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

This paper concerns the application of Huber-based robust unscented Kalman filter (HRUKF) in nonlinear system with non-Gaussian measurement noise. The tuning factor $$\gamma $$γ is key factor in determining the form of Huber cost function. Traditionally, $$\gamma $$γ 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 $$\gamma $$γ 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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