A comparison of probabilistic representations for decentralised data fusion

Ong, Lee-Ling, Ridley, Matthew, Upcroft, Ben, Kumar, Suresh, Bailey, Tim, Sukkarieh, Salah and Durrant-Whyte, Hugh (2005). A comparison of probabilistic representations for decentralised data fusion. In: , Proceedings of the 2005 Intelligent Sensors, Sensor Networks and Information Processing Conference. Intelligent Sensors, Sensor Networks and Information Processing Conference, 2005, Melbourne, Australia, (187-192). 5-8 December 2005.


Author Ong, Lee-Ling
Ridley, Matthew
Upcroft, Ben
Kumar, Suresh
Bailey, Tim
Sukkarieh, Salah
Durrant-Whyte, Hugh
Title of paper A comparison of probabilistic representations for decentralised data fusion
Conference name Intelligent Sensors, Sensor Networks and Information Processing Conference, 2005
Conference location Melbourne, Australia
Conference dates 5-8 December 2005
Proceedings title Proceedings of the 2005 Intelligent Sensors, Sensor Networks and Information Processing Conference
Place of Publication Piscataway NJ, USA
Publisher IEEE
Publication Year 2005
Sub-type Fully published paper
ISBN 0-7803-9399-6
Start page 187
End page 192
Total pages 6
Language eng
Abstract/Summary This paper compares and contrasts three different probabilistic models - Particle representations, Parzen density estimates, and Gaussian mixture models - for non-Gaussian, non-linear feature tracking, when applied to multiple autonomous vehicles using the Decentralised Data Fusion (DDF) paradigm. These probabilistic models were chosen as they are all capable of approximating the probability distributions of an ideal Bayesian filter and have different properties with regard to computational efficiency and quality of the approximation. In order to show that each model satisfy the DDF requirements of modularity, scalability and robustness, the performance of each representation is taken from a simulation for multi-sensor bearing-only tracking. Performance is evaluated in three areas: (a) mathematical accuracy and optimality of fusion for correlated information between nodes, (b) computational efficiency and accuracy of various operations in the DDF framework and (c) bandwidth requirements for communicating the representations over a wireless network.
Subjects 0913 Mechanical Engineering
091303 Autonomous Vehicles
Keyword Particle representations
Parzen density estimates
Gaussian mixture models
non-Gaussian
autonomous vehicles
Decentralised Data Fusion (DDF)
Intelligent sensors
Q-Index Code EX
Additional Notes 2nd International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP 2005)

 
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Created: Wed, 23 Dec 2009, 14:05:33 EST by Maria Campbell on behalf of Faculty Of Engineering, Architecture & Info Tech