The fusion of oblique-incidence ionograms gathered from multiple collection sites

Author

Fisher, Robert ; Fulcher, John ; Richards, Shane ; Hagenbuchner, Marlcus ; Coleman, Chris

Author_Institution

Dept. of Comput. Sci., Wollongong Univ., NSW, Australia

fYear

1996

fDate

21-22 Nov 1996

Firstpage

125

Lastpage

130

Abstract

Artificial neural networks are highly suited to the application of data fusion. This paper presents a range of multilayer feedforward network structures designed to perform parameter extraction from simulated ionograms. The experimental results show an eightfold improvement on nondata fusion techniques, and also allow for the extraction of gradient information. This demonstrates the ability of data fusion to improve the accuracy of a system, and to produce information that would otherwise be unavailable

Keywords

atmospheric techniques; feedforward neural nets; geophysical signal processing; ionospheric electromagnetic wave propagation; ionospheric techniques; multilayer perceptrons; sensor fusion; artificial neural networks; data fusion; gradient information extraction; multilayer feedforward neural network structures; multiple collection sites; oblique-incidence ionogram fusion; parameter extraction; Backpropagation; Computational modeling; Computer science; Data mining; Frequency; Ionosphere; Neural networks; Parameter extraction; Sensor fusion; Sensor systems;

fLanguage

English

Publisher

ieee

Conference_Titel

Data Fusion Symposium, 1996. ADFS '96., First Australian

Conference_Location

Adelaide, SA

Print_ISBN

0-7803-3601-1

Type

conf

DOI

10.1109/ADFS.1996.581094

Filename

581094