IMPROVING THE INVERSION OF IONOGRAMS BY COMBINING NEURAL-NETWORK AND DATA FUSION TECHNIQUES

Data fusion (integration) techniques are combined with Multi-Layer Fee dforward (backpropagation) neural networks in order to improve the inv ersion extraction of the key describing parameters - of oblique-incide nce ionograms (plots of apparent height of reflection versus transmiss ion frequency). Two separate investigations were undertaken: first the incorporation of vertical ionogram data to improve inversion; and sec ondly, the fusion of ionogram data gathered from a 2D array of ionoson des (the ground-based radio frequency transmitters). With the former, the average percentage errors obtained by incorporating data fusion dr opped by a factor of five when compared with single ionogram inversion . Moreover, gradients of ionospheric parameters (critical frequency, l ayer height and thickness) were also obtained. In the case of the latt er the error rate dropped by a similar factor and by even more when ve rtical ionograms were incorporated Better results were forthcoming whe n a hierarchical network was used to invert the ionograms prior to fus ion, compared with directly fusing the ionogram array data.