A cross-validation approach to trajectory and shape reconstruction for rigid bodies

Jieqi Yu, Haipeng Zheng, Sanjeev R. Kulkarni, H. Vincent Poor

Research output: Chapter in Book/Report/Conference proceedingConference contribution

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 languageEnglish (US)
Title of host publicationProceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010
Pages307-312
Number of pages6
DOIs
StatePublished - Nov 24 2010
Event2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 - Kittila, Finland
Duration: Aug 29 2010Sep 1 2010

Publication series

NameProceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010

Other

Other2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010
CountryFinland
CityKittila
Period8/29/109/1/10

All Science Journal Classification (ASJC) codes

  • Human-Computer Interaction
  • Signal Processing

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  • Cite this

    Yu, J., Zheng, H., Kulkarni, S. R., & Poor, H. V. (2010). A cross-validation approach to trajectory and shape reconstruction for rigid bodies. In 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