Short-data recursive HMM parameter estimation for rapid vision-based aircraft heading estimation

Molloy, Timothy L. & Ford, Jason J. (2014) Short-data recursive HMM parameter estimation for rapid vision-based aircraft heading estimation. In Proceedings of the 2014 Australian Control Conference, IEEE, Australian National University, Canberra, ACT, pp. 60-65.

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Abstract

Rapid recursive estimation of hidden Markov Model (HMM) parameters is important in applications that place an emphasis on the early availability of reasonable estimates (e.g. for change detection) rather than the provision of longer-term asymptotic properties (such as convergence, convergence rate, and consistency). In the context of vision- based aircraft (image-plane) heading estimation, this paper suggests and evaluates the short-data estimation properties of 3 recursive HMM parameter estimation techniques (a recursive maximum likelihood estimator, an online EM HMM estimator, and a relative entropy based estimator). On both simulated and real data, our studies illustrate the feasibility of rapid recursive heading estimation, but also demonstrate the need for careful step-size design of HMM recursive estimation techniques when these techniques are intended for use in applications where short-data behaviour is paramount.

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ID Code: 78795
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
DOI: 10.1109/AUCC.2014.7358683
ISBN: 978-1-4673-6749-3
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Control Systems Robotics and Automation (090602)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Funding:
Copyright Owner: Copyright 2014 [please consult the author]
Deposited On: 19 Nov 2014 22:28
Last Modified: 22 Mar 2016 04:10

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