Extended Target Tracking Using Gaussian Processes

Date

2015-08-15

Author

Wahlström, Niklas
Özkan, Emre

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

In this paper, we propose using Gaussian processes to track an extended object or group of objects, that generates multiple measurements at each scan. The shape and the kinematics of the object are simultaneously estimated, and the shape is learned online via a Gaussian process. The proposed algorithm is capable of tracking different objects with different shapes within the same surveillance region. The shape of the object is expressed analytically, with well-defined confidence intervals, which can be used for gating and association. Furthermore, we use an efficient recursive implementation of the algorithm by deriving a state space model in which the Gaussian process regression problem is cast into a state estimation problem.

Subject Keywords

Extended target tracking, Gaussian processes, Star-convex.

URI

https://hdl.handle.net/11511/33280

Collections

Department of Electrical and Electronics Engineering, Article