An important foundation for various vehicular applications is a reliable environment recognition. In this context, the simultaneous estimation of the state and the existence of an unknown number of objects under difficult detection conditions is a particular challenge. In this paper, we propose an algorithm for tracking extended objects under clutter. We propose an extended measurement model which enables the estimation of the object width using a standard Kalman filter implementation without the need for clustering the data. As this implies multiple observations generated by one object and additional clutter observations, the generalized probabilistic data association with a state-depended cardinality model is utilized. The proposed algorithm is evaluated with simulated data of a radar-based vehicle tracking system.
Radar-based extended object tracking under clutter using generalized probabilistic data association
01.10.2013
357388 byte
Aufsatz (Konferenz)
Elektronische Ressource
Englisch
Generalized Probabilistic Data Association for Vehicle Tracking under Clutter
British Library Conference Proceedings | 2012
|Generalized radar clutter model
IEEE | 1999
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