SUMMARY

Loran-C is a land-based navigation system with a high repeatable accuracy. However, precise positioning requires the distortions of Loran-C signal propagation to be compensated carefully. This paper shows, how neural networks can be used to learn the signal distortions by GPS data. A simulation shows that this approach can reach nearly the same positioning quality than GPS. Therefore, neural network calibrated Loran-C can effectively aid GPS in cases when GPS availability is weak.