Information Regularized Sensor Fusion: Application to Localization With Distributed Motion Sensors |
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| Authors: | Umut Ozertem and Deniz Erdogmus |
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| Affiliation: | (1) CSEE Department, Oregon Health and Science University, Portland, OR, USA |
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| Abstract: | We propose the information regularization principle for fusing information from sets of identical sensors observing a target
phenomenon. The principle basically proposes an importance-weighting scheme for each sensor measurement based on the mutual
information based pairwise statistical similarity matrix between sensors. The principle is applied to maximum likelihood estimation
and particle filter based state estimation. A demonstration of the proposed regularization scheme in centralized data fusion
of dense motion detector networks for target tracking is provided. Simulations confirm that the introduction of information
regularization significantly improves localization accuracy of both maximum likelihood and particle filter approaches compared
to their baseline implementations. Outlier detection and sensor failure detection capabilities, as well as possible extensions
of the principle to decentralized sensor fusion with communication constraints are briefly discussed.
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| Keywords: | information regularization principle mutual information target localization and tracking binary motion detector network |
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