Compressed sensing using hidden Markov models with application to vision based aircraft tracking

Ford, Jason J., Molloy, Timothy L., & Hall, Joanne L. (2014) Compressed sensing using hidden Markov models with application to vision based aircraft tracking. In 17th International Conference on Information Fusion, 7-10 July 2014, Salamanca, Spain.


This paper investigates compressed sensing using hidden Markov models (HMMs) and hence provides an extension of recent single frame, bounded error sparse decoding problems into a class of sparse estimation problems containing both temporal evolution and stochastic aspects. This paper presents two optimal estimators for compressed HMMs. The impact of measurement compression on HMM filtering performance is experimentally examined in the context of an important image based aircraft target tracking application. Surprisingly, tracking of dim small-sized targets (as small as 5-10 pixels, with local detectability/SNR as low as − 1.05 dB) was only mildly impacted by compressed sensing down to 15% of original image size.

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ID Code: 73893
Item Type: Conference Paper
Refereed: Yes
Additional URLs:
Keywords: hidden Markov model, compressed sensing, target tracking
Subjects: Australian and New Zealand Standard Research Classification > ENGINEERING (090000) > ELECTRICAL AND ELECTRONIC ENGINEERING (090600) > Signal Processing (090609)
Divisions: Current > Schools > School of Electrical Engineering & Computer Science
Current > Schools > School of Mathematical Sciences
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 Please consult the authors
Deposited On: 14 Jul 2014 22:46
Last Modified: 29 Oct 2014 23:05

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