一种基于神经网络的罗兰C ASF修正方法

Neural Network Based Loran-C ASF Correction Method

影响罗兰C导航定位精度的主要因素为信号在传播过程中产生的ASF(附加二次相位因子)传播时延,通过对ASF的修正可以提升罗兰C的定位精度。首先将罗兰C信号覆盖区域栅格化,通过对神经网络进行训练,找到大地电导率、大气折射率、温度、湿度与ASF修正量之间的关系。最后把得到的理论预测值与实际值进行对比,结果表明该方法有效。

The main factor that affects Loran-C navigation positioning accuracy is the ASF propagation delay generated during signal propagation. The accuracy of Loran-C positioning can be improved by correcting the ASF. First, the Loran-C signal coverage area was rasterized and then the relationship between earth conductivity, atmospheric refractive index, temperature, humidity and ASF correction was found through training of neural network. Finally, the theoretical predicted value is compared with the actual value, and the results show that method is effective.