Sensor failure detection capabilities in low-level fusion: A comparison between fuzzy voting and Kalman filtering

This paper focuses on the comparison of the low-level sensor failure detection capabilities of model-based Kalman filtering and a model-free Fuzzy voting approach. The ability to identify failing and degrading sensors is essential when dealing with error-prone data acquired in harsh application environments as can be typically found in the field of embedded systems. In order to investigate the respective performance concerning rejection of faulty data several experiments were conducted in a simulation environment. The two candidates were selected to gain more insight into the advantages and limitations of system modeling (or the lack thereof) in signal-level data fusion. The Kalman filter was selected as a typical candidate that relies on extensive models for both the system and information sources. The fuzzy approach, however, employs a heuristic that requires no modeling at all. This results in a broader field of possible applications since detailed knowledge is no longer required. Thus, it can be employed in scenarios that one would not be able to use a model-based algorithm. Such applications include scenarios with ongoing reconfiguration (e.g. wireless sensor networks) or systems with limited detail knowledge about the devices.