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

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

doi:10.1109/MLSP.2010.5589198

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 proceeding › Conference 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 language	English (US)
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	https://doi.org/10.1109/MLSP.2010.5589198
State	Published - 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

Other

Other	2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010
Country/Territory	Finland
City	Kittila
Period	8/29/10 → 9/1/10

All Science Journal Classification (ASJC) codes

Human-Computer Interaction
Signal Processing

Access to Document

10.1109/MLSP.2010.5589198

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). Article 5589198 (Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010). https://doi.org/10.1109/MLSP.2010.5589198

Yu, Jieqi ; Zheng, Haipeng ; Kulkarni, Sanjeev R. et al. / A cross-validation approach to trajectory and shape reconstruction for rigid bodies. Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010. 2010. pp. 307-312 (Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010).

@inproceedings{fd5ba145f1ab46eb8220238c505d145d,

title = "A cross-validation approach to trajectory and shape reconstruction for rigid bodies",

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.",

author = "Jieqi Yu and Haipeng Zheng and Kulkarni, {Sanjeev R.} and Poor, {H. Vincent}",

year = "2010",

doi = "10.1109/MLSP.2010.5589198",

language = "English (US)",

isbn = "9781424478774",

series = "Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010",

pages = "307--312",

booktitle = "Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010",

note = "2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010 ; Conference date: 29-08-2010 Through 01-09-2010",

}

Yu, J, Zheng, H, Kulkarni, SR & Poor, HV 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., 5589198, Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010, pp. 307-312, 2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010, Kittila, Finland, 8/29/10. https://doi.org/10.1109/MLSP.2010.5589198

A cross-validation approach to trajectory and shape reconstruction for rigid bodies. / Yu, Jieqi; Zheng, Haipeng; Kulkarni, Sanjeev R. et al.
Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010. 2010. p. 307-312 5589198 (Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A cross-validation approach to trajectory and shape reconstruction for rigid bodies

AU - Yu, Jieqi

AU - Zheng, Haipeng

AU - Kulkarni, Sanjeev R.

AU - Poor, H. Vincent

PY - 2010

Y1 - 2010

N2 - 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.

AB - 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.

UR - http://www.scopus.com/inward/record.url?scp=78449271888&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=78449271888&partnerID=8YFLogxK

U2 - 10.1109/MLSP.2010.5589198

DO - 10.1109/MLSP.2010.5589198

M3 - Conference contribution

AN - SCOPUS:78449271888

SN - 9781424478774

T3 - Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010

SP - 307

EP - 312

BT - Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010

T2 - 2010 IEEE 20th International Workshop on Machine Learning for Signal Processing, MLSP 2010

Y2 - 29 August 2010 through 1 September 2010

ER -

Yu J, Zheng H, Kulkarni SR , Poor HV. 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. 2010. p. 307-312. 5589198. (Proceedings of the 2010 IEEE International Workshop on Machine Learning for Signal Processing, MLSP 2010). doi: 10.1109/MLSP.2010.5589198

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

Abstract

Publication series

Other

All Science Journal Classification (ASJC) codes

Access to Document

Other files and links

Fingerprint

Cite this