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Comparison of filters in target tracking
Benko, Matej ; Bednář, Josef (referee) ; Žák, Libor (advisor)
The topic of this bachelor thesis is Optimal Bayesian estimate usage in target tracking with bistatic measurement. The thesis is focused on particle filtering. It is shown particle filters are effective algorithms providing Optimal Bayesian estimate solution. There are tested and evaluated many types of fundamental algorithms, like SIR, Auxiliary or Regularized particle filters. It is compared to the accuracy of optimal estimates on various situations, depend on a different trajectory or acceleration of the target.
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Kernel Methods in Particle Filtering
Coufal, David ; Beneš, Viktor (advisor)
Kernel Methods in Particle Filtering David Coufal Doctoral thesis - abstract The thesis deals with the use of kernel density estimates in particle filtering. In particular, it examines the convergence of the kernel density estimates to the filtering densities. The estimates are constructed on the basis of an out- put from particle filtering. It is proved theoretically that using the standard kernel density estimation methodology is effective in the context of particle filtering, although particle filtering does not produce random samples from the filtering densities. The main theoretical results are: 1) specification of the upper bounds on the MISE error of the estimates of the filtering densities and their partial derivatives; 2) specification of the related lower bounds and 3) providing a suitable tool for checking persistence of the Sobolev character of the filtering densities over time. In addition, the thesis also focuses on designing kernels suitable for practical use. 1
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Comparison of filters in target tracking
Benko, Matej ; Bednář, Josef (referee) ; Žák, Libor (advisor)
The topic of this bachelor thesis is Optimal Bayesian estimate usage in target tracking with bistatic measurement. The thesis is focused on particle filtering. It is shown particle filters are effective algorithms providing Optimal Bayesian estimate solution. There are tested and evaluated many types of fundamental algorithms, like SIR, Auxiliary or Regularized particle filters. It is compared to the accuracy of optimal estimates on various situations, depend on a different trajectory or acceleration of the target.
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Kernel Methods in Particle Filtering
Coufal, David ; Beneš, Viktor (advisor)
Kernel Methods in Particle Filtering David Coufal Doctoral thesis - abstract The thesis deals with the use of kernel density estimates in particle filtering. In particular, it examines the convergence of the kernel density estimates to the filtering densities. The estimates are constructed on the basis of an out- put from particle filtering. It is proved theoretically that using the standard kernel density estimation methodology is effective in the context of particle filtering, although particle filtering does not produce random samples from the filtering densities. The main theoretical results are: 1) specification of the upper bounds on the MISE error of the estimates of the filtering densities and their partial derivatives; 2) specification of the related lower bounds and 3) providing a suitable tool for checking persistence of the Sobolev character of the filtering densities over time. In addition, the thesis also focuses on designing kernels suitable for practical use. 1
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Kernel Methods in Particle Filtering
Coufal, David ; Beneš, Viktor (advisor) ; Klebanov, Lev (referee) ; Studený, Milan (referee)
Kernel Methods in Particle Filtering David Coufal Doctoral thesis - abstract The thesis deals with the use of kernel density estimates in particle filtering. In particular, it examines the convergence of the kernel density estimates to the filtering densities. The estimates are constructed on the basis of an out- put from particle filtering. It is proved theoretically that using the standard kernel density estimation methodology is effective in the context of particle filtering, although particle filtering does not produce random samples from the filtering densities. The main theoretical results are: 1) specification of the upper bounds on the MISE error of the estimates of the filtering densities and their partial derivatives; 2) specification of the related lower bounds and 3) providing a suitable tool for checking persistence of the Sobolev character of the filtering densities over time. In addition, the thesis also focuses on designing kernels suitable for practical use. 1
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Pravděpodobnostní modely pro lokalizaci bezpilotního letounu testované na reálných datech
Figura, Juraj ; Vomlelová, Marta (advisor) ; Obdržálek, David (referee)
The thesis addresses the dynamic state estimation problem for the field of robotics, particularly for unmanned aerial vehicles (UAVs). Based on data collected from an UAV, we design several probabilistic models for estimation of its state (mainly speed and rotation angles), including the configurations where one of the sensors is not available. We use Kalman filter and Particle filter and focus on learning the model parameters using EM algorithm. The EM algorithm is then adjusted with respect to non-Gaussian density of some sensor errors and modified using model complexity penalization terms for better generalization. We implement these methods in MATLAB environment and evaluate on separate datasets. We also analyze data from a ground robot and use our implementation of Particle filter for estimation of its position. Powered by TCPDF (www.tcpdf.org)
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