An α-Rényi Divergence Sigmoïd Parametrization ...
Document type :
Autre communication scientifique (congrès sans actes - poster - séminaire...): Communication dans un congrès avec actes
Title :
An α-Rényi Divergence Sigmoïd Parametrization For a Multi-Objectives and Context-Adaptive Fault Tolerant Localization
Author(s) :
Harbaoui, Nesrine [Auteur]
Makkawi, Khoder [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Tmazirte, Nourdine [Auteur]
El Badaoui El Najjar, Maan [Auteur]
Makkawi, Khoder [Auteur]
Centre de Recherche en Informatique, Signal et Automatique de Lille - UMR 9189 [CRIStAL]
Tmazirte, Nourdine [Auteur]
El Badaoui El Najjar, Maan [Auteur]
Conference title :
24th International Conference on Information Fusion
City :
Sun City
Country :
Afrique du Sud
Start date of the conference :
2021-11-01
English keyword(s) :
Multi sensor fusion
localization
context-aware diagnostic
fault detection and isolation
optimized threshold
GNSS
INS
localization
context-aware diagnostic
fault detection and isolation
optimized threshold
GNSS
INS
HAL domain(s) :
Informatique [cs]/Théorie de l'information [cs.IT]
Informatique [cs]/Robotique [cs.RO]
Sciences de l'ingénieur [physics]/Automatique / Robotique
Informatique [cs]/Robotique [cs.RO]
Sciences de l'ingénieur [physics]/Automatique / Robotique
English abstract : [en]
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 ...
Show more >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.Show less >
Show more >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.Show less >
Language :
Anglais
Peer reviewed article :
Oui
Audience :
Internationale
Popular science :
Non
Collections :
Source :