Adaptive Kriging Particle Filter and its Application to Terrain-Aided Navigation
Bastien Hubert, Karim Dahia, Nicolas Merlinge, Audrey Giremus · 2024
In GNSS-denied and poorly-known environments, reliable autonomous navigation is a major challenge, as conventional data fusion algorithms require an extensive knowledge of their surroundings to accurately estimate the vehicle state. To address this issue, we propose to use an adaptive Gaussian process regression to model an approximation of the environment solely based on scarce and noisy samples. This paper takes advantage of the flexibility of Gaussian processes to dynamically model the underlying terrain by adapting the process to the relevant data at each step. To this end, we propose to locally fit the Gaussian process and perform regression by using only a subset of data points selected according to a proximity criterion. The developed method employs a regularised particle filter to effectively estimate the system state using the output of the regression. By integrating Gaussian process-based terrain predictions, the particle filter can effectively compensate for the lack of precise terrain information, thus enhancing navigation performance in GNSS-denied scenarios. To evaluate the effectiveness of the proposed approach, simulations were performed in terrain-aided navigation of an unmanned aerial vehicle. Comparative analysis with existing navigation methods illustrates the superiority of the proposed approach in terms of accuracy and robustness.