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Title: Particle tracking using the unscented Kalman filter in high energy physics experiments
Authors: Akhtar, Jahanzeb
Advisors: Powell, R
Kyberd, P
Keywords: Particle tracking;Unscented Kalman filter;Relative observability;Smoothing;Muon ionization cooling experiment
Issue Date: 2015
Publisher: Brunel University London
Abstract: The extended Kalman lter (EKF) has a long history in the field of non-linear tracking. More recently, statistically-based estimators have emerged that avoid the need for a deterministic linearisation process. The Unscented Kalman filter (UKF) is one such technique that has been shown to perform favourably for some non-linear systems when compared to an EKF implementation, both in terms of accuracy and robustness. In this Thesis, the UKF is applied to a high energy physics particle tracking problem where currently the EKF is being implemented. The effects of measurement redundancy are investigated to determine improvements in accuracy of particle track reconstruction. The relationship between measurement redundancy and relative observability is also investigated through an experimental and theoretical analysis. Smoothing (backward filtering), in the high energy physics experiments, is implementedusing the Rauch Tung Striebel (RTS) smoother with the EKF , however, in Unscented Kalman filter algorithms, the Jacobian matrices required by the RTS method, are not available. The Unscented Rauch Tung Striebel (URTS) smoother addresses this problem by avoiding the use of Jacobian matrices but is not effi cient for large dimensional systems such as high energy physics experiments. A technique is implemented in the RTS smoother to make it suitable for the UKF. The method is given the name the Jacobian Equivalent Rauch Tung Striebel (JE-RTS) smoother. The implementation of this method is quite straight forward when the UKF is used as an estimator.
Description: This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University London.
Appears in Collections:Electronic and Computer Engineering
Dept of Electronic and Computer Engineering Theses

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