The Solution of the Dynamics Prediction of Aircraft State

Xiu Ying Zhao; Ji Cheng Quan; Jing Yuan Wei; Yu Liu; Ke Xiong Chen

doi:10.4028/www.scientific.net/AMM.190-191.209

Paper Titles

The Application of BP Neural Network to Comprehensive Assessment in Equipment Maintainance Support
p.191

The Construction of Security Supervision Platform for Fireworks and Firecrackers Based on IOT
p.195

The Designment of Digital Greenhouse Expert System
p.199

The Research of System Security Based on .NET Platform
p.205

The Solution of the Dynamics Prediction of Aircraft State
p.209

The TCA of Hypoid Gears Based on MATLAB
p.213

The Uncertain Programming of the Closed-Loop Supply Chain with Remanufacturing System
p.218

Three Dimensional Numerical Analysis of Plasma Spraying Using Supersonic Plasma Gun
p.222

Using Contextual Words for Object Detection
p.227

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 190-191The Solution of the Dynamics Prediction of...

The Solution of the Dynamics Prediction of Aircraft State

Abstract:

To monitor aircraft flight status, predict the position of aircraft is needed. By the research of the kalman filter, particle filter, and the best curve fitting method, use the last before time or several before moments‘s flight state data to predict the state of the aircraft, even forward time’s state, the dynamically weighted average of the multi-times data can get to the aircraft status data and subsequent flight status data, so the aircraft fly normally and smoothly with the predicted status data, that can get a good visual effect. The algorithm experiment is through the extraction data from the real aircraft data, the average relative error is 0.03%.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 190-191)

Pages:

209-212

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.190-191.209

Citation:

Cite this paper

Online since:

July 2012

Authors:

Xiu Ying Zhao, Ji Cheng Quan, Jing Yuan Wei, Yu Liu, Ke Xiong Chen

Keywords:

EKF, Estimation, PF, Prediction, UKF

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] Rekleitis I. M. A particle filter tutorial for mobile robot localization. Montreal, Quebec, Canada: Centre for Intelligent Machines, McGill University, Technical Report: TM-CIM-04-02, (2004).

Google Scholar

[2] Deng Xiaolong, Xie Jianying, Guo Weizhong. Bayesian target tracking based on particle filter. Journal of Systems Engineering and Electronics, 2005, 16(3): 545~549.

Google Scholar

[3] Chang Cheng, Ansari Rashid. Kernel particle filter for visual tracking. IEEE Signal Processing Letters, 2005, 12(3): 242~245.

DOI: 10.1109/lsp.2004.842254

Google Scholar

[4] Kalsson Rickard. Particle filtering for positioning and tracking applications [D]. Department of Electrical Engineering, Linköpings Universitet, (2005).

Google Scholar

[5] Fox D., Hightower J., Liao L., Schulz D., Borriello G. Bayesian filtering for location estimation. IEEE Pervasive Computing, 2003, 2(3): 24~33. 12. Kwok C., Fox D., Meila M. Real-Time particle filters. Proceedings of the IEEE, 2004, 92(3): 469~484.

DOI: 10.1109/mprv.2003.1228524

Google Scholar

[6] Julier S. J., Uhlmann J. K. Unscented filtering and nonlinear estimation. Proceedings of the IEEE, 2004, 92(3): 401~422.

DOI: 10.1109/jproc.2003.823141

Google Scholar

[7] Welch G., Bishop G. An introduction to the kalman filer. University of North Carolina at Chapel Hill: Technical Report, TR 95-041, (2004).

Google Scholar