Extended Object Tracking with Random Hypersurface Models

@article{Baum2014ExtendedOT,
  title={Extended Object Tracking with Random Hypersurface Models},
  author={Marcus Baum and Uwe D. Hanebeck},
  journal={IEEE Transactions on Aerospace and Electronic Systems},
  year={2014},
  volume={50},
  pages={149-159}
}
  • M. Baum, U. Hanebeck
  • Published 18 April 2013
  • Mathematics
  • IEEE Transactions on Aerospace and Electronic Systems
The random hypersurface model (RHM) is introduced for estimating a shape approximation of an extended object in addition to its kinematic state. An RHM represents the spatial extent by means of randomly scaled versions of the shape boundary. In doing so, the shape parameters and the measurements are related via a measurement equation that serves as the basis for a Gaussian state estimator. Specific estimators are derived for elliptic and star-convex shapes. 
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References

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It is shown that uniformly distributed measurement sources on an ellipse lead to a uniformly distributed squared scaling factor and a Bayesian inference mechanisms tailored to elliptic shapes is introduced, which is also suitable for scenarios with high measurement noise.
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A Gaussian-assumed Bayesian tracking method that provides the means to track and estimate shapes of multiple extended targets is derived andSimulations demonstrate the performance of the new approach.
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Student research highlight: Simultaneous tracking and shape estimation of extended targets
  • M. Baum
  • Engineering
    IEEE Aerospace and Electronic Systems Magazine
  • 2012
TLDR
The author looks at the random hypersurface model and its role in tracking; the tracking method is evaluated using a Microsoft® Kinect™ sensor as an example.
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  • M. Baum, U. Hanebeck
  • Computer Science
    IEEE Transactions on Aerospace and Electronic Systems
  • 2012
TLDR
A novel approach for extended object tracking with no statistical assumptions about the location of the measurement sources on the extended target object is presented and a combined set-theoretic and stochastic estimator is obtained that is robust to systematic errors in the target model.
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