Multi-sensor fault recovery in the presence of known and unknown fault types

This paper proposes an efficient online, hybrid, Bayesian multi-sensor fusion algorithm for target tracking in the presence of modelled and unmodelled faults. The algorithm comprises two stages. The first stage attempts to remove modelled faults from each individual sensor estimate. The second stage...

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Veröffentlicht in:2009 12th International Conference on Information Fusion : 6-9 July 2009 S. 1695 - 1703
Hauptverfasser: Reece, S., Roberts, S., Claxton, C., Nicholson, D.
Format: Tagungsbericht
Sprache:Englisch
Veröffentlicht: IEEE 01.07.2009
Schlagworte:
Bayesian methods
Computer architecture
Fault detection
fault detection and recovery
Filtering
Kalman filter
multi-sensor data fusion
Robots
Sensor fusion
Sensor systems
State estimation
Target tracking
Voting
ISBN:9780982443804, 0982443803
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Beschreibung
Zusammenfassung:This paper proposes an efficient online, hybrid, Bayesian multi-sensor fusion algorithm for target tracking in the presence of modelled and unmodelled faults. The algorithm comprises two stages. The first stage attempts to remove modelled faults from each individual sensor estimate. The second stage de-emphasises estimates which have been subject to unanticipated faults and are still faulty despite undergoing the Stage 1 fault recovery process. The algorithm is a computationally efficient and decentralisable hybrid of two standard approaches to fault detection, namely model-based fault detection and majority voting. The algorithm is tested on two distinct simulated scenarios (1) when the target process model does not match reality and (2) in the presence of simultaneous modelled and unanticipated faults.
ISBN:9780982443804
0982443803