### Abstract

In this paper, a method is proposed for reconstructing the trajectory and shape of a rigid body in a damped environment from distributively collected, asynchronous data. In this problem setting, both the shape parameters of the rigid body and its trajectory are unknown. The shape/trajectory recovery problem is modeled as a minimization of energy dissipation under geometric and acceleration constraints. In order to solve this problem, a convex relaxation for the geometric constraint is introduced, and the geometric constraint is reinforced in a cross-validation stage to verify the parameters. In this manner the shape and the trajectory of the rigid body are reconstructed simultaneously. For simplicity, a two-dimensional ball is taken as the rigid body prototype and simulations demonstrate the efficacy of the algorithm.

Original language | English (US) |
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Title of host publication | Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010 |

Pages | 307-312 |

Number of pages | 6 |

DOIs | |

State | Published - Nov 24 2010 |

Event | 2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 - Kittila, Finland Duration: Aug 29 2010 → Sep 1 2010 |

### Publication series

Name | Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010 |
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### Other

Other | 2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 |
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Country | Finland |

City | Kittila |

Period | 8/29/10 → 9/1/10 |

### All Science Journal Classification (ASJC) codes

- Human-Computer Interaction
- Signal Processing

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## Cite this

*Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010*(pp. 307-312). [5589198] (Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010). https://doi.org/10.1109/MLSP.2010.5589198