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The Marginalized Particle Filter in Practice
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology.
2006 (English)In: Proceedings of the 2006 IEEE Aerospace Conference, 2006Conference paper, Published paper (Refereed)
Abstract [en]

The marginalized particle filter is a powerful combination of the particle filter and the Kalman filter, which can be used when the underlying model contains a linear sub-structure, subject to Gaussian noise. This paper will illustrate several positioning and target tracking applications, solved using the marginalized particle filter. Furthermore, we analyze several properties of practical importance, such as its computational complexity and how to cope with quantization effects.

Place, publisher, year, edition, pages
2006.
Keyword [en]
Gaussian noise, Adaptive Kalman filters, Computational complexity, Particle filtering (numerical methods), Position control, Quantisation (signal), Target tracking, Linear sub-structure, Marginalized particle filter, Positioning, Quantization effects
National Category
Engineering and Technology Control Engineering
Identifiers
URN: urn:nbn:se:liu:diva-13922DOI: 10.1109/AERO.2006.1655922ISBN: 9780780395459 (print)OAI: oai:DiVA.org:liu-13922DiVA: diva2:22195
Conference
2006 IEEE Aerospace Conference, Big Sky, MT, USA, March, 2006
Funder
Vinnova
Available from: 2006-09-04 Created: 2006-09-04 Last updated: 2013-04-07
In thesis
1. Estimation of Nonlinear Dynamic Systems: Theory and Applications
Open this publication in new window or tab >>Estimation of Nonlinear Dynamic Systems: Theory and Applications
2006 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

This thesis deals with estimation of states and parameters in nonlinear and non-Gaussian dynamic systems. Sequential Monte Carlo methods are mainly used to this end. These methods rely on models of the underlying system, motivating some developments of the model concept. One of the main reasons for the interest in nonlinear estimation is that problems of this kind arise naturally in many important applications. Several applications of nonlinear estimation are studied.

The models most commonly used for estimation are based on stochastic difference equations, referred to as state-space models. This thesis is mainly concerned with models of this kind. However, there will be a brief digression from this, in the treatment of the mathematically more intricate differential-algebraic equations. Here, the purpose is to write these equations in a form suitable for statistical signal processing.

The nonlinear state estimation problem is addressed using sequential Monte Carlo methods, commonly referred to as particle methods. When there is a linear sub-structure inherent in the underlying model, this can be exploited by the powerful combination of the particle filter and the Kalman filter, presented by the marginalized particle filter. This algorithm is also known as the Rao-Blackwellized particle filter and it is thoroughly derived and explained in conjunction with a rather general class of mixed linear/nonlinear state-space models. Models of this type are often used in studying positioning and target tracking applications. This is illustrated using several examples from the automotive and the aircraft industry. Furthermore, the computational complexity of the marginalized particle filter is analyzed.

The parameter estimation problem is addressed for a relatively general class of mixed linear/nonlinear state-space models. The expectation maximization algorithm is used to calculate parameter estimates from batch data. In devising this algorithm, the need to solve a nonlinear smoothing problem arises, which is handled using a particle smoother. The use of the marginalized particle filter for recursive parameterestimation is also investigated.

The applications considered are the camera positioning problem arising from augmented reality and sensor fusion problems originating from automotive active safety systems. The use of vision measurements in the estimation problem is central to both applications. In augmented reality, the estimates of the camera’s position and orientation are imperative in the process of overlaying computer generated objects onto the live video stream. The objective in the sensor fusion problems arising in automotive safety systems is to provide information about the host vehicle and its surroundings, such as the position of other vehicles and the road geometry. Information of this kind is crucial for many systems, such as adaptive cruise control, collision avoidance and lane guidance.

Place, publisher, year, edition, pages
Institutionen för systemteknik, 2006
Series
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 998
Series
Keyword
Nonlinear estimation, system identification, Kalman filter, particle filter, marginalized particle filter, expectation maximization, automotive applications
National Category
Control Engineering
Identifiers
urn:nbn:se:liu:diva-7124 (URN)91-85497-03-7 (ISBN)
Public defence
2006-02-02, Visionen, Hus B, Campus Valla, Linköpings universitet, Linköping, 10:15 (English)
Opponent
Supervisors
Available from: 2006-09-04 Created: 2006-09-04 Last updated: 2009-06-04

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Schön, ThomasKarlsson, RickardGustafsson, Fredrik

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