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Overcomplete Radon bases for target property management in sensor networks

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4 Author(s)
Xiaoye Jiang ; Stanford University, Stanford, CA, 94305 ; Mo Li ; Yuan Yao ; Leonidas J. Guibas

This paper presents a scalable algorithm for managing property information about moving objects tracked by a sensor network. Property information is obtained via distributed sensor observations, but will be corrupted when objects mix up with each other. The association between properties and objects then becomes ambiguous. We build a novel representation framework, exploiting an overcomplete Radon basis dictionary to model property uncertainty in such circumstances. By making use of the combinatorial structure of the basis design and sparse representations we can efficiently approximate the underlying probability distribution of the association between target properties and tracks, overcoming the exponential space that would otherwise be required. We conduct comparative simulations and the results validate the effectiveness of our approach.

Published in:

Information Processing in Sensor Networks (IPSN), 2011 10th International Conference on

Date of Conference:

12-14 April 2011