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Environmental boundary tracking and estimation using multiple autonomous vehicles

Jin, Zhipu and Bertozzi, Andrea L. (2008) Environmental boundary tracking and estimation using multiple autonomous vehicles. In: 46th IEEE Conference on Decision and Control. No.Procee. IEEE , Piscataway, NJ, pp. 4918-4923. ISBN 978-1-4244-1497-0.

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In this paper, we develop a framework for environmental boundary tracking and estimation by considering the boundary as a hidden Markov model (HMM) with separated observations collected from multiple sensing vehicles. For each vehicle, a tracking algorithm is developed based on Page’s cumulative sum algorithm (CUSUM), a method for change-point detection, so that individual vehicles can autonomously track the boundary in a density field with measurement noise. Based on the data collected from sensing vehicles and prior knowledge of the dynamic model of boundary evolvement, we estimate the boundary by solving an optimization problem, in which prediction and current observation are considered in the cost function. Examples and simulation results are presented to verify the efficiency of this approach.

Item Type:Book Section
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Additional Information:© 2007 IEEE. The authors would like to thank Prof. Boris Rozovsky, Dr. Alexander Tartakovsky, and Ernie Esser for discussions and comments. This research is partly supported by ARO MURI grant 50363-MA-MURI and ONR grant N000140610059.
Funding AgencyGrant Number
Army Research Office - Multidisciplinary University Initiative (ARO MURI)50363-MA-MURI
Office of Naval Research (ONR)N000140610059
Subject Keywords:boundary tracking and estimation; hidden Markov model; change-point detection; CUSUM; optimization
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INSPEC Accession Number9885955
Issue or Number:Procee
Record Number:CaltechAUTHORS:20100923-142446484
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Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:20106
Deposited By: Tony Diaz
Deposited On:24 Sep 2010 20:12
Last Modified:08 Nov 2021 23:57

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