Online hidden Markov model parameter estimation and minimax robust quickest change detection in uncertain stochastic processes

Molloy, Timothy Liam (2015) Online hidden Markov model parameter estimation and minimax robust quickest change detection in uncertain stochastic processes. PhD thesis, Queensland University of Technology.

Abstract

Stochastic (or random) processes are inherent to numerous fields of human endeavour including engineering, science, and business and finance. This thesis presents multiple novel methods for quickly detecting and estimating uncertainties in several important classes of stochastic processes. The significance of these novel methods is demonstrated by employing them to detect aircraft manoeuvres in video signals in the important application of autonomous mid-air collision avoidance.

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Full-text downloads:

54 since deposited on 14 Dec 2015
54 in the past twelve months

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ID Code: 88476
Item Type: QUT Thesis (PhD)
Supervisor: Ford, Jason & Upcroft, Ben
Additional Information: Recipient of 2015 Outstanding Doctoral Thesis Award
Keywords: Stochastic Processes, hidden Markov model, parameter estimation, quickest change detection, minimax robust, CUSUM rule, Shiryaev rule, relative entropy, manoeuvre detection, least favourable distributions, ODTA
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Institution: Queensland University of Technology
Deposited On: 14 Dec 2015 05:41
Last Modified: 15 Apr 2016 00:25

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