$\alpha$ - Rényi based framework for a Robust and Fault-Tolerant Localization
Khoder Makkawi, Nesrine Harbaoui, Nourdine Aït Tmazirte, Maan El Badaoui El Najjar · 2021
A robust criterion with an adaptive diagnosis for a fail-safe localization system is presented in the paper. To achieve robustness, the Minimum Error Entropy (MEE) is the criterion combined with the Unscented Information Filter (UIF) creating the MEEUIF as a robust estimator in the information theory. As known, the Unscented Transformation (UT) deals well with nonlinearity problems, but the performance decreases significantly under the presence of non-Gaussian noises. The MEE overcomes this problem and shows high robustness dealing with heavy non-Gaussian noises (especially multi-Gaussian noises). For Fault Detection and Isolation (FDI), the$\alpha$-Rényi divergence$(\alpha$-RD) is proposed offering an adaptive diagnosis layer able to interact with the surrounding environment of the system. Then, the decision is executed based on an adaptive threshold determined through the α- Renyi criterion$(\alpha$- Rc). The proposed approach is tested and validated using real experimental data, for a multi-sensor fusion of Global Navigation Satellite System (GNSS), and Odometer (Odo) data for an autonomous vehicle localization application. The main contributions of the paper are: - The development of a robust multi-sensor fusion using the MEEUIF, - The design of an adaptive diagnosis layer based on - Renyi divergence, and - The validation of the proposed approach using real experimental data.