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Paper
3 April 2000 Decision forgetting and decision smoothing for diagnostic decision fusion in systems with redundant information
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Abstract
This paper introduces techniques to deal with temporal aspects of fusion systems with redundant information. One of the challenges of a fusion system is that individual information is not necessarily announced at the same time. While some decisions (or features or data) are produced at a high sampling frequency, other decisions are generated at a much lower rate, perhaps only once during the operation of the system or only during certain operating conditions. This means that some information will be outdated when the actual information fusion task is performed. An event may have occurred in the meantime leading to a decision discord. We tackle this challenge by introducing the concept of `information or decision forgetting'. In other words, in case of an information discord, more recent information is evaluated with higher confidence than older information. Another difficulty is distinguishing between outliers and actual system changes. If tools perform their task at a high sampling frequency we can employ `decision smoothing'. That is, we factor out the occasional outlier and generally reduce the noise of the system. To that end, we introduce an adaptive smoothing algorithm that evaluates the system state and changes the smoothing parameter if it encounters suspicious situations, i.e., situations that might indicate a changed system state. We show the concepts introduced in the diagnostic realm where we aggregate the output of several different diagnostic tools.
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Kai Goebel "Decision forgetting and decision smoothing for diagnostic decision fusion in systems with redundant information", Proc. SPIE 4051, Sensor Fusion: Architectures, Algorithms, and Applications IV, (3 April 2000); https://doi.org/10.1117/12.381659
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