A Study of Information Fusion for UAV Based on RBF Neural Network

Author

Dongli, Yuan ; Jianguo, Yan ; Xinmin, Wang ; Qingbiao, Xi

Author_Institution

Northwestern Polytech Univ., Xian

fYear

2007

fDate

May 30 2007-June 1 2007

Firstpage

2839

Lastpage

2842

Abstract

We all know that it is very difficult to create accurate model for UAV navigation system because that this system is nonlinear system, at the same time, the environment information provided by information sources of multi-sensor in UAV is uncertain. Correspondingly, neural network can provide precise navigation information for UAV by fusing multi-source information that is uncertain, incomplete and mutually exclusive, accordingly to ensure navigation precise. This paper put forwards an information fusion method for UAV integrated navigation system based on RBF neural network. The simulation results show that this method can provide satisfactory navigation information.

Keywords

aerospace computing; aircraft navigation; military computing; radial basis function networks; remotely operated vehicles; sensor fusion; RBF neural network; airbone multi sensor; information fusion; neural network; nonlinear system; unmanned aerial vehicle navigation; Automation; Cameras; Neural networks; Nonlinear systems; Radio navigation; Satellite navigation systems; Sensor systems; State estimation; Synthetic aperture radar; Unmanned aerial vehicles; RBF neural network; UAV (Unmanned Aerial Vehicle); integrated navigation; multisensor information fusion;

fLanguage

English

Publisher

ieee

Conference_Titel

Control and Automation, 2007. ICCA 2007. IEEE International Conference on

Conference_Location

Guangzhou

Print_ISBN

978-1-4244-0818-4

Electronic_ISBN

978-1-4244-0818-4

Type

conf

DOI

10.1109/ICCA.2007.4376880

Filename

4376880