New methods for decentralised sensor fusion and extended target tracking models

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2022-12-16

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Köksal, Hilal

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The focus of this thesis is two-fold; the first study investigates the use of student-t distribution for the decentralised multi-sensor fusion problem. Multi-sensor fusion can suffer from several artefacts such as low channel capacity, delays in the communication channels, the correlation in the acquired information and sensor biases. When combined with these artefacts, the errors in the local sensors’ estimates can result in conflicting information at the fusion center. Such conflicts may later be resolved by future observations. Traditional Gaussian fusion can perform poorly in such cases due to its inherent uni-modal assumption. We propose incorporating student-t distribution while performing multi-sensor fusion, which introduces the ability to represent the uncertainty due to conflicting sensor information. Another focus of this thesis is an alternative measurement update framework for Gaussian process-based extended target tracking models. The proposed method performs variational inference in the measurement update step to improve the accuracy of the kinematic and extent state estimates. The performance evaluations of the methods are presented by conducting various simulations and real data experiments.

Subject Keywords

Multi sensor fusion, Student t-distribution, Extended target tracking, Gaussian process, Variational Bayes

URI

https://hdl.handle.net/11511/102079

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

H. Köksal, “New methods for decentralised sensor fusion and extended target tracking models,” M.S. - Master of Science, Middle East Technical University, 2022.