Author:
Michael E. Farmer
Affiliation:
University of Michigan-Flint, United States
Keyword(s):
Sensor fusion, Dempster-shafer, Kalman filtering, Belief updating.
Related
Ontology
Subjects/Areas/Topics:
Decision Support Systems
;
Informatics in Control, Automation and Robotics
;
Intelligent Control Systems and Optimization
;
Sensors Fusion
;
Signal Processing, Sensors, Systems Modeling and Control
Abstract:
The traditional D-S conditioning is based on a collection of ‘experts’ inputting their evidence and accumulating the beliefs. Researchers have often adopted this same mechanism for integrating evidence from single sources of evidence over time, such as seen in sensor networks. One issue with this approach is that the order of inputs does not matter. While this is sensible for a collection of experts we propose that it is not suitable for a single input providing streams of evidence. Likewise research in psychology show order of integration of evidence does matter, and depending on the application humans have a preference for recency or primacy. Estimation theory provides frameworks for analyzing data over time, and recently some researchers have proposed integrating evidence in an estimation-inspired manner. In light of this we propose a Kalman-filter based approach for integrating single sensor evidence over time where the evidence conflict plays the role of system noise in ada
pting the filter gain.
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