Decentralised Data Fusion with Parzen Density Estimates

Ridley, Matthew, Upcroft, Ben, Ong, Lee Ling, Kumar, Suresh, & Sukkarieh, Salah (2004) Decentralised Data Fusion with Parzen Density Estimates. In Palaniswami, M, Krishnamachari, Bhaskar, Sowmya, Arcot, & Challa, Subhash (Eds.) Proceedings of the 2004 Intelligent Sensors, Sensor Networks and Information Processing Conference, 2004, IEEE, Melbourne, Australia, pp. 161-166.

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Decentralised sensor networks typically consist of multiple processing nodes supporting one or more sensors. These nodes are interconnected via wireless communication. Practical applications of Decentralised Data Fusion have generally been restricted to using Gaussian based approaches such as the Kalman or Information Filter This paper proposes the use of Parzen window estimates as an alternate representation to perform Decentralised Data Fusion. It is required that the common information between two nodes be removed from any received estimates before local data fusion may occur Otherwise, estimates may become overconfident due to data incest. A closed form approximation to the division of two estimates is described to enable conservative assimilation of incoming information to a node in a decentralised data fusion network. A simple example of tracking a moving particle with Parzen density estimates is shown to demonstrate how this algorithm allows conservative assimilation of network information.

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ID Code: 40429
Item Type: Conference Paper
Refereed: Yes
Keywords: Gaussian distributions, moving particle tracking, multiple processing nodes, decentralised sensor networks, decentralised data fusion, Parzen density estimates, Parzen window estimates, closed form approximation, conservative assimilation, wireless communication, Australia
DOI: 10.1109/ISSNIP.2004.1417455
ISBN: 0-7803-8894-1
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2004 IEEE
Copyright Statement: Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
Deposited On: 10 May 2011 04:31
Last Modified: 27 Apr 2014 17:10

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