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 -