Improving the inversion of ionograms by combining neural network and data fusion techniques

Abstract

Data fusion (integration) techniques are combined with MultiLayer Feedforward (backpropagation) neural networks in order to improve the inversion —extraction of the key describing parameters — of oblique-incidence ionograms (plots of apparent height of reflection versus transmission frequency). Two separate investigations were undertaken: first, the incorporation of vertical ionogram data to improve inversion; and secondly, the fusion of ionogram data gathered from a 2D array of ionosondes (the ground-based radio frequency transmitters). With the former, the average percentage errors obtained by incorporating data fusion dropped by a factor of five when compared with single ionogram inversion. Moreover, gradients of ionospheric parameters (critical frequency, layer height and thickness) were also obtained. In the case of the latter, the error rate dropped by a similar factor, and by even more when vertical ionograms were incorporated. Better results were forthcoming when a hierarchical network was used to invert the ionograms prior to fusion, compared with directly fusing the ionogram array data.