Adaptive neural networks-fuzzy reasoning information fusion system

Author

Qingwei, Sun ; Zhilu, Wu ; Taifan, Quan

Author_Institution

Dept. of Electron. & Commun. Eng., Harbin Inst. of Technol., China

Volume

1

fYear

2002

fDate

28-31 Oct. 2002

Firstpage

712

Abstract

This paper presents an adaptive neural networks-fuzzy reasoning information fusion system which is employed to decrease the influence of the uncertainty of sensor state on fusion performance under a complex environment. The model consists of a confidence estimator, fusing weight knowledge base and weighted fusion. When utilizing the new model, we can obtain the confidence of each sensor and the reliable fusion data with an adaptive synthetical computation of sensor state, sensor detection precision and track filtering error based on the techniques of neural networks, fuzzy reasoning and knowledge base. The simulation results show that the new fusion model is obviously advantageous compared with the conventional Kalman weighted fusion.

Keywords

adaptive signal processing; fuzzy set theory; inference mechanisms; neural nets; sensor fusion; uncertainty handling; adaptive neural networks-fuzzy reasoning information fusion system; adaptive synthetical computation; complex environment; confidence estimator; fusing weight knowledge base; fusion model; sensor confidence; sensor detection precision; track filtering error; uncertainty sensor state; weighted fusion; Adaptive filters; Adaptive systems; Computational modeling; Computer networks; Filtering; Fuzzy reasoning; Neural networks; Sensor fusion; Sensor systems; Uncertainty;

fLanguage

English

Publisher

ieee

Conference_Titel

TENCON '02. Proceedings. 2002 IEEE Region 10 Conference on Computers, Communications, Control and Power Engineering

Print_ISBN

0-7803-7490-8

Type

conf

DOI

10.1109/TENCON.2002.1181373

Filename

1181373