Approximate Bayesian algorithms for multiple target tracking with binary sensors

In this paper, we propose an approximate Bayesian computation approach to perform a multiple target tracking within a binary sensor network. The nature of the binary sensors (getting closer - moving away information) do not allow the use of the classical tools (e.g. Kalman Filter, Particle Filer), b...

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Published inarXiv.org
Main Author Ickowicz, Adrien
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 16.10.2014
Subjects
Algorithms
Bayesian analysis
Kalman filters
Multiple target tracking
Position tracking
Sensors
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Abstract In this paper, we propose an approximate Bayesian computation approach to perform a multiple target tracking within a binary sensor network. The nature of the binary sensors (getting closer - moving away information) do not allow the use of the classical tools (e.g. Kalman Filter, Particle Filer), because the exact likelihood is intractable. To overcome this, we use the particular feature of the likelihood-free algorithms to produce an efficient multiple target tracking methodology.
AbstractList In this paper, we propose an approximate Bayesian computation approach to perform a multiple target tracking within a binary sensor network. The nature of the binary sensors (getting closer - moving away information) do not allow the use of the classical tools (e.g. Kalman Filter, Particle Filer), because the exact likelihood is intractable. To overcome this, we use the particular feature of the likelihood-free algorithms to produce an efficient multiple target tracking methodology.
Author Ickowicz, Adrien
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Snippet In this paper, we propose an approximate Bayesian computation approach to perform a multiple target tracking within a binary sensor network. The nature of the...
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SubjectTerms Algorithms
Bayesian analysis
Kalman filters
Multiple target tracking
Position tracking
Sensors
Title Approximate Bayesian algorithms for multiple target tracking with binary sensors
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