A Novel Data Fusion Method Based on Signal's Reliability

Ming Xiao; Liang Pan; Yan Long Bu; Li Li Wan

doi:10.4028/www.scientific.net/AMM.321-324.561

Paper Titles

Robust Multisensor Image Matching Using Bayesian Estimated Mutual Information
p.541

ARMA-AKF Model of MEMS Gyro Rotation Data Random Drift Compensation
p.549

Cultural Design Method on Multi-Sensor Technology Design: A Case of Interactive Lamp Design
p.553

The Application of Fiber Bragg Grating Sensors in High Voltage Switchgear Contact Temperature Measurement Monitoring System
p.558

A Novel Data Fusion Method Based on Signal's Reliability
p.561

An Efficient Semidefinite Relaxation Method for Energy-Based Source Localization in Sensor Networks
p.568

RFID-Based Thermal Convection Floating Type Accelerometer with Different Packaging Chambers and Filled Gases
p.573

Vertex-Distinguishing Total Coloring of mC₅
p.578

Study on Simulation System of Incremental Photoelectric Encoder
p.582

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 321-324A Novel Data Fusion Method Based on Signal's...

A Novel Data Fusion Method Based on Signal's Reliability

Abstract:

The traditional Kalman filter is able to obtain the optimal estimation of the estimated signals. However, it fails to consider their reliability. In real applications, the estimated signals may include outliers. Fortunately, we are able to know the reliability of the signals transcendentally. In this paper, we derive the one-dimensional data fusion formulas based on signals reliability which is according to minimum variance restriction. Furthermore, a corresponding data fusion scheme is proposed. Experimental results show the propose data fusion method performs much better than traditional methods.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 321-324)

Pages:

561-567

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.321-324.561

Citation:

Cite this paper

Online since:

June 2013

Authors:

Ming Xiao, Liang Pan, Yan Long Bu, Li Li Wan

Keywords:

Data Fusion, Kalman Filter (KF), Minimum Variance

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] BARHAM, P.M., and HUMPHRIES,D.E：Derivation of the Kalman filtering equations form elementary statistical principles. NATO Agardograph AG139.Theory and application of Kalman filters,(1970)

Google Scholar

[2] GELB,A.(Ed.): Applied optimal estimation.　Massachusetts ．Institute of Technology, Cambrige, MA, (1974)

Google Scholar

[3] Yang Xin, Yang Xin. Infrared and Laser Engineeing, Vol. 23(2003),pp.231-233

Google Scholar

[4] D. H.Titterton. J. L.Weston. Strapdown inertial navigation technology.(2007)

Google Scholar

[5] Patrick M. Fitzpatrick. Advanced calculus: a course in mathematical analysis.(1996)

Google Scholar