Distributed Detection and Data Fusion
Distributed Detection and Data Fusion
A two-tier data dissemination model for large-scale wireless sensor networks
Proceedings of the 8th annual international conference on Mobile computing and networking
Habitat monitoring: application driver for wireless communications technology
ACM SIGCOMM Computer Communication Review - Workshop on data communication in Latin America and the Caribbean
Unobtrusive monitoring of computer interactions to detect cognitive status in elders
IEEE Transactions on Information Technology in Biomedicine
Data association based on optimization in graphical models with application to sensor networks
Mathematical and Computer Modelling: An International Journal
Audio-visual group recognition using diffusion maps
IEEE Transactions on Signal Processing
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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.