ASF seasonal correction of Loran-C based on artificial neural network

In this paper, neural networks are used to decrease the Additional Secondary Phase Factors (ASF) error of Loran-C to improve the navigation accuracy. Through the training, the relationship between ASF corrections and seasons can be obtained, which is useful to compensate for the measured time-difference (TD) of Loran-C wave. The result proves that this method is effective and provides a new way for ASF correction.