A comparative evaluation of conventional and particle filter based radar target tracking

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2007

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Yıldırım, Berkin

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In this thesis the radar target tracking problem in Bayesian estimation framework is studied. Traditionally, linear or linearized models, where the uncertainty in the system and measurement models is typically represented by Gaussian densities, are used in this area. Therefore, classical sub-optimal Bayesian methods based on linearized Kalman filters can be used. The sequential Monte Carlo methods, i.e. particle filters, make it possible to utilize the inherent non-linear state relations and non-Gaussian noise models. Given the sufficient computational power, the particle filter can provide better results than Kalman filter based methods in many cases. A survey over relevant radar tracking literature is presented including aspects as estimation and target modeling. In various target tracking related estimation applications, particle filtering algorithms are presented.

Subject Keywords

Electrical engineering.

URI

http://etd.lib.metu.edu.tr/upload/12609043/index.pdf
https://hdl.handle.net/11511/17327

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Graduate School of Natural and Applied Sciences, Thesis

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Citation Formats

B. Yıldırım, “A comparative evaluation of conventional and particle filter based radar target tracking,” M.S. - Master of Science, Middle East Technical University, 2007.