Composite Kalman Motion Estimation Method Based on Virtual Distance

Authors

Zhizhong Sun, School of Computer Science & Engineering, Nanjing University of Science & Technology, Nanjing 210094, China;
Zehui Lu, School of Computer Science & Engineering, Nanjing University of Science & Technology, Nanjing 210094, China;
Weiqing Li, School of Computer Science & Engineering, Nanjing University of Science & Technology, Nanjing 210094, China;

Abstract

Abstract: MEMS inertial sensors applied to human motion capture, but because of the MEMS sensor system errors, the virtual people cannot operate precisely and stably. In order to achieve tracking the movement of an arm accurately, by analyzing the movement of a virtual arm, measuring the distance between the finger of the arm and the center of the object in virtual scene, combined with the virtual arm movement characteristics, a composite Kalman motion estimation method was designed. The model divided arm movement into a uniform model, a uniformly accelerated model and several typical phase models. Depending on the different distance measure, used the different models to make an accurate estimate of the movement of a virtual arm. Through experiments, the composite Kalman motion estimation method could estimate virtual arms in a virtual scene accurately.

Recommended Citation

Sun, Zhizhong; Lu, Zehui; and Li, Weiqing (2020) "Composite Kalman Motion Estimation Method Based on Virtual Distance," Journal of System Simulation: Vol. 28: Iss. 10, Article 34.
Available at: https://dc-china-simulation.researchcommons.org/journal/vol28/iss10/34

First Page

2534

Revised Date

2016-07-14

DOI Link

https://doi.org/

Last Page

2539

CLC

TP391.9

Recommended Citation

Sun Zhizhong, Lu Zehui, Li Weiqing. Composite Kalman Motion Estimation Method Based on Virtual Distance[J]. Journal of System Simulation, 2016, 28(10): 2534-2539.