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


Autoria(s): Molloy, Timothy Liam
Data(s)

2015

Resumo

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.

Formato

application/pdf

Identificador

http://eprints.qut.edu.au/88476/

Publicador

Queensland University of Technology

Relação

http://eprints.qut.edu.au/88476/1/Timothy_Molloy_Thesis.pdf

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.

Fonte

School of Electrical Engineering & Computer Science; Science & Engineering Faculty

Palavras-Chave #Stochastic Processes #hidden Markov model #parameter estimation #quickest change detection #minimax robust #CUSUM rule #Shiryaev rule #relative entropy #manoeuvre detection #least favourable distributions #ODTA
Tipo

Thesis