A congeneric multi-sensor data fusion algorithm and its fault-tolerance

Li, Shuqing; Sheng-xiu, Zhang

doi:10.1109/iccasm.2010.5619284

Cited by 5 publications

(4 citation statements)

References 4 publications

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“…In this method the data fusion merges N intervals to estimate the smallest one tolerating f hardware faults. In [21] an adaptive technique was used to weight the sensor outputs in multisensor data fusion process. This technique uses the standard deviation of each sensor (estimated by statistics and a temporal factor related to previous data) to calculate a discounting factor of the sensor outputs and uses it in data fusion process to ensure fault tolerance and weaken the negative eects of aging hardware.…”

Section: Fault Tolerance In Data Fusionmentioning

confidence: 99%

A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localization

Bader

Lussier

Schön

2017

Robotics and Autonomous Systems

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Multi-sensor perception have an important role in robotics and autonomous systems, as inputs for critical functions such as obstacle detection, localization, etc. This Multi-sensor perception begins to appear in critical applications, such as drones and ADAS (Advanced Driver Assistance Systems). However such complex systems are dicult to validate entirely. In this paper we study these systems under an alternative dependability method: fault tolerance. We propose an approach to tolerate faults in multi-sensor data fusion based on the more classical method of duplication-comparison, and oering detection and recovery services. We detail an example implementation using Kalman lters data fusion for mobile robot localization. We demonstrate its eectiveness in this case study using real data and fault injection.

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Section: Fault Tolerance In Data Fusionmentioning

confidence: 99%

A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localization

Bader

Lussier

Schön

2017

Robotics and Autonomous Systems

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“…The works in [20,21] proposed the analysis of the static and dynamic reliability of data sources to detect the defective source. Other similar examples of fault tolerance in data fusion are mentioned in [22,23].…”

Section: Introductionmentioning

confidence: 96%

“…As an alternative to this validation, some works developed fault tolerance mechanisms for data fusion to remove or reduce the effects of faults on the system or process operation [8], [9]. Fault Tolerance is defined as one of the four means to attain dependability [10] and aims to allow the system to deliver a correct service even after the occurrence of errors [11,12].…”

Section: Introductionmentioning

confidence: 99%

An Informational Approach for Fault Tolerant Data Fusion Applied to a UAV’s Attitude, Altitude, and Position Estimation

et al. 2021

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This paper presents a fault tolerance architecture for data fusion mechanisms that tolerates sensor faults in a multirotor Unmanned Aerial Vehicle (UAV). The developed approach is based on the traditional duplication/comparison method and is carried out via error detection and system recovery to both detect and isolate the faulty sensors. It is applied on an informational framework using extended Informational Kalman Filters (IKF) for state estimation with prediction models based on available sensors measurements. Error detection is realized through residuals comparisons using the Bhattacharyya Distance (BD), an informational measure that estimates the similarity of two probability distributions. An optimal thresholding based on Bhattacharyya criterion is applied. In order to identify the faulty sensor, the Bhattacharyya distance between the a priori and a posteriori distributions of each IKF is also computed. The system recovery is done by substituting the erroneous state by an error-free state. The proposed architecture alleviates the assumption of a fault-free prediction model using the information surprise concept instead of hardware redundancy.The performance of the proposed framework is shown through offline validation using real measurements from navigation sensors of a multirotor UAV with fault injection.

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“…Accordingly, if the conflict of the source is high when compared to the other sources, it is considered to be faulty and it is isolated before the final fusion process. An adaptive technique to weight the outputs sensors is presented in [ 12 ]. Where, authors estimates the standard deviation of each sensor by statistics and a time factor related to the previous data, then mitigate the impact of of hardware degradation by calculating the sensor output update factor for the data fusion process.…”

Section: Introductionmentioning

confidence: 99%

Adaptive Diagnosis for Fault Tolerant Data Fusion Based on α-Rényi Divergence Strategy for Vehicle Localization

Makkawi

Ait-Tmazirte

Najjar

et al. 2021

Entropy

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When applying a diagnostic technique to complex systems, whose dynamics, constraints, and environment evolve over time, being able to re-evaluate the residuals that are capable of detecting defaults and proposing the most appropriate ones can quickly prove to make sense. For this purpose, the concept of adaptive diagnosis is introduced. In this work, the contributions of information theory are investigated in order to propose a Fault-Tolerant multi-sensor data fusion framework. This work is part of studies proposing an architecture combining a stochastic filter for state estimation with a diagnostic layer with the aim of proposing a safe and accurate state estimation from potentially inconsistent or erroneous sensors measurements. From the design of the residuals, using α-Rényi Divergence (α-RD), to the optimization of the decision threshold, through the establishment of a function that is dedicated to the choice of α at each moment, we detail each step of the proposed automated decision-support framework. We also dwell on: (1) the consequences of the degree of freedom provided by this α parameter and on (2) the application-dictated policy to design the α tuning function playing on the overall performance of the system (detection rate, false alarms, and missed detection rates). Finally, we present a real application case on which this framework has been tested. The problem of multi-sensor localization, integrating sensors whose operating range is variable according to the environment crossed, is a case study to illustrate the contributions of such an approach and show the performance.

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A congeneric multi-sensor data fusion algorithm and its fault-tolerance

Cited by 5 publications

References 4 publications

A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localization

A fault tolerant architecture for data fusion: A real application of Kalman filters for mobile robot localization

An Informational Approach for Fault Tolerant Data Fusion Applied to a UAV’s Attitude, Altitude, and Position Estimation

Adaptive Diagnosis for Fault Tolerant Data Fusion Based on α-Rényi Divergence Strategy for Vehicle Localization

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