Tracking and data association
Approximation of functions over redundant dictionaries using coherence
SODA '03 Proceedings of the fourteenth annual ACM-SIAM symposium on Discrete algorithms
Tracking a moving object with a binary sensor network
Proceedings of the 1st international conference on Embedded networked sensor systems
A line in the sand: a wireless sensor network for target detection, classification, and tracking
Computer Networks: The International Journal of Computer and Telecommunications Networking - Special issue: Military communications systems and technologies
Trio: enabling sustainable and scalable outdoor wireless sensor network deployments
Proceedings of the 5th international conference on Information processing in sensor networks
Design and Implementation of a Dual-Camera Wireless Sensor Network for Object Retrieval
IPSN '08 Proceedings of the 7th international conference on Information processing in sensor networks
Fourier Theoretic Probabilistic Inference over Permutations
The Journal of Machine Learning Research
Canopy closure estimates with GreenOrbs: sustainable sensing in the forest
Proceedings of the 7th ACM Conference on Embedded Networked Sensor Systems
A distributed algorithm for managing multi-target identities in wireless ad-hoc sensor networks
IPSN'03 Proceedings of the 2nd international conference on Information processing in sensor networks
Beyond trilateration: on the localizability of wireless ad hoc networks
IEEE/ACM Transactions on Networking (TON)
A Reliability-Oriented Transmission Service in Wireless Sensor Networks
IEEE Transactions on Parallel and Distributed Systems
Optimizing event detection in low duty-cycled sensor networks
Wireless Networks
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This article 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. Based on the proposed theories, we design a fully distributed algorithm on wireless sensor networks. We conduct comparative simulations and the results validate the effectiveness of our approach.