JSM 2005 - Toronto

Abstract #302702

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Legend: = Applied Session, = Theme Session, = Presenter
Activity Number: 11
Type: Invited
Date/Time: Sunday, August 7, 2005 : 2:00 PM to 3:50 PM
Sponsor: IMS
Abstract - #302702
Title: Distributed State Representation for Tracking Problems in Sensor Networks
Author(s): Juan Liu*+ and Maurice Chu and James Reich
Companies: Palo Alto Research Center and Palo Alto Research Center and Palo Alto Research Center
Address: 3333 Coyote Hill Rd, Palo Alto, CA, 94304,
Keywords: multi-target tracking ; identity management ; collaborative sensor networks ; bayesian inference
Abstract:

This paper investigates the problem of designing decentralized representations to support monitoring and inferences in sensor networks. While commonly used in signal processing and control, state-space models of physical phenomena such as those arising from tracking multiple interacting targets suffer from the curse of dimensionality as the number of phenomena of interest increases. Furthermore, mapping an inference algorithm onto a distributed sensor network must appropriately allocate scarce sensing and communication resources. We address the state-space explosion by developing a distributed state-space model that switches between factored and joint state spaces as appropriate. We develop a collaborative group abstraction as a mechanism to effectively support the information flow within and across subspaces of the state-space model that can be efficiently supported in a communication-constrained network. The approach has been implemented and demonstrated in a simulation of tracking multiple interacting targets.

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Revised March 2005