An α-Rényi Divergence Sigmoïd Parametrization For a Multi-Objectives and Context-Adaptive Fault Tolerant Localization

Abstract

For a localization function, meeting together safety, accuracy and availability is a challenging task. Targeting one of these Key Performance Indicators (KPIs) remains feasible but when one or more other requirements are expected at the same time, the objectives become antagonistic. To achieve accuracy, a multi-sensor data fusion is recommended. However, it remains insufficient when it comes to safety critical applications as autonomous vehicle. Indeed, a diagnostic layer has to be considered to treat the presence of faults in dynamic environment, which can affect the sensors measurements. The detection algorithm must ensure high fault sensitivity while keeping false alarm rate as low as possible and taking into account both the change of navigation context and the change of targeted KPIs. This paper proposes a GNSS (Global Navigation Satellite System) and INS (Inertial Navigation system) data fusion approach based on an unscented information filter for state estimation boosted by an adaptive diagnostic layer consisting of a Fault Detection and Isolation (FDI) method based on a powerful parametric information divergence: the α-Rényi divergence. The concept of diagnosis adaptability is developed by applying a sigmoïd strategy in order to increase the sensitivity of the selected residual to detect maximum of faults according to the crossed environment. The suitable selection, at each instant, of α, is ensured through the implementation of a generalized logistic function according to the current constraint of the navigation context. Following the detection step, a decision-cost optimized threshold is reevaluated at each instant. Applied to field data, the first experiments show promising results of the developed framework compared to a diagnostic layer based on the well-known Kullback-Leibler divergence.