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Extended Target Tracking Using Polynomials With Applications to Road-Map Estimation
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
Linköping University, Department of Electrical Engineering, Automatic Control. Linköping University, The Institute of Technology. (Sensor Fusion)
2011 (English)In: IEEE Transactions on Signal Processing, ISSN 1053-587X, E-ISSN 1941-0476, Vol. 59, no 1, 15-26 p.Article in journal (Refereed) Published
Abstract [en]

This paper presents an extended target tracking framework which uses polynomials in order to model extended objects in the scene of interest from imagery sensor data. State-space models are proposed for the extended objects which enables the use of Kalman filters in tracking. Different methodologies of designing measurement equations are investigated. A general target tracking algorithm that utilizes a specific data association method for the extended targets is presented. The overall algorithm must always use some form of prior information in order to detect and initialize extended tracks from the point tracks in the scene. This aspect of the problem is illustrated on a real life example of road-map estimation from automotive radar reports along with the results of the study.

Place, publisher, year, edition, pages
IEEE Signal Processing Society, 2011. Vol. 59, no 1, 15-26 p.
Keyword [en]
Automotive radar, EIV, Data association, Errors in output, Errors in variables, Extended target tracking, Parabola, Polynomial, Road map
National Category
Signal Processing Control Engineering
URN: urn:nbn:se:liu:diva-63831DOI: 10.1109/TSP.2010.2081983ISI: 000285519200002OAI: diva2:383079
Swedish Foundation for Strategic Research
Available from: 2011-01-13 Created: 2011-01-04 Last updated: 2013-09-23Bibliographically approved
In thesis
1. Sensor Fusion for Automotive Applications
Open this publication in new window or tab >>Sensor Fusion for Automotive Applications
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Mapping stationary objects and tracking moving targets are essential for many autonomous functions in vehicles. In order to compute the map and track estimates, sensor measurements from radar, laser and camera are used together with the standard proprioceptive sensors present in a car. By fusing information from different types of sensors, the accuracy and robustness of the estimates can be increased.

Different types of maps are discussed and compared in the thesis. In particular, road maps make use of the fact that roads are highly structured, which allows relatively simple and powerful models to be employed. It is shown how the information of the lane markings, obtained by a front looking camera, can be fused with inertial measurement of the vehicle motion and radar measurements of vehicles ahead to compute a more accurate and robust road geometry estimate. Further, it is shown how radar measurements of stationary targets can be used to estimate the road edges, modeled as polynomials and tracked as extended targets.

Recent advances in the field of multiple target tracking lead to the use of finite set statistics (FISST) in a set theoretic approach, where the targets and the measurements are treated as random finite sets (RFS). The first order moment of a RFS is called probability hypothesis density (PHD), and it is propagated in time with a PHD filter. In this thesis, the PHD filter is applied to radar data for constructing a parsimonious representation of the map of the stationary objects around the vehicle. Two original contributions, which exploit the inherent structure in the map, are proposed. A data clustering algorithm is suggested to structure the description of the prior and considerably improving the update in the PHD filter. Improvements in the merging step further simplify the map representation.

When it comes to tracking moving targets, the focus of this thesis is on extended targets, i.e., targets which potentially may give rise to more than one measurement per time step. An implementation of the PHD filter, which was proposed to handle data obtained from extended targets, is presented. An approximation is proposed in order to limit the number of hypotheses. Further, a framework to track the size and shape of a target is introduced. The method is based on measurement generating points on the surface of the target, which are modeled by an RFS.

Finally, an efficient and novel Bayesian method is proposed for approximating the tire radii of a vehicle based on particle filters and the marginalization concept. This is done under the assumption that a change in the tire radius is caused by a change in tire pressure, thus obtaining an indirect tire pressure monitoring system.

The approaches presented in this thesis have all been evaluated on real data from both freeways and rural roads in Sweden.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 93 p.
Linköping Studies in Science and Technology. Dissertations, ISSN 0345-7524 ; 1409
Kalman filter, PHD filter, extended targets, tracking, sensor fusion, road model, single track model, bicycle model
National Category
Signal Processing
urn:nbn:se:liu:diva-71594 (URN)978-91-7393-023-9 (ISBN)
Public defence
2011-11-25, Key 1, Hus Key, Campus Valla, Linköpings universitet, Linköping, 13:15 (English)
Available from: 2011-10-26 Created: 2011-10-24 Last updated: 2011-11-08Bibliographically approved

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