The paper presents a fault-tolerant multi-sensor fusion approach with Fault Detection and Exclusion (FDE) based on information theory. The Maximum Correntropy Criterion (MCC) in Unscented Information Filter (UIF) form, called (MCCUIF), is used as estimator. The Unscented Transformation (UT) provides an efficient tool to restrict the non-linear state estimation problem. However, the UIF works well with Gaussian noises, where its performance may decrease when dealing with non-Gaussian noises. The MCC is used to deal with non-Gaussian noises (for instance shot noises or Gaussian mixture noises). For detection and exclusion of erroneous measurements, a residual is designed using a- Rényi Divergence (a-RD) between a priori and a posteriori probabilities distributions. Then α-Rényi criterion (a-Rc) is used in the decision part of the proposed approach in order to calculate an adaptive threshold for FDE. In order to target both high integrity and accuracy of the navigation function of an autonomous vehicle in stringent environments (urban canyon, forests ...), this paper presents a tightly coupled architecture by merging raw data of a Global Navigation Satellite System (GNSS) with odometer (odo) measurements through the proposed approach. The main contributions of this paper are: - the proposition of a multisensor fusion approach using MCCUIF, - the development of an FDE method using a residual based on a-RD with an adequate choice of a value and adaptive thresholding, - the validation of the proposed approach with real experimental data.

This paper presents a strategy to combine a fault tolerant multi-sensor data fusion with a closed loop controller scheme robust against external disturbances applied to a multi-robot mobile system tracking different trajectories. Multi-sensor fusion is ensured using an information filter which is the canonical form of the Kalman filter. Fault detection and exclusion strategy is proposed to eliminate any erroneous measurements, for this purpose, risiduals are generated using the Kullback-Leibler diveregence that compares the priori and posteriori distributions of the predicted and the corrected estimations respectively. Prediction model is based on odometry, with encoders data as input. Observation model is based on extra sensors observations. To optimise detections, an adaptive thresholding method based on the Kullback-Leibler Criteron is proposed. Trajectory tracking is achieved using a sliding mode controller (SMC), developped by inverting the dynamical model of mobile robots. SMC is robust against external matched disturbances, parameters variation and actuators detoriation, however it can not handle total loss of effectiveness, which makes detection and isolation of faulty actuators compulsory. For this purpose, controllers inputs are converted into expected elementary velocities and compared to real data obtained from fault tolerant multi-sensor data fusion. The main contribution of this paper is to combine a fault detection and exclusion (FDE) scheme with an enhanced sliding mode controller in order to detect and isolate both sensors and actuators faults. The method is applied to a multi-robot system. The obtained experimental results demonstrate the egibility and the effectiveness of the proposed approach.