基于神经网络的GPS周跳探测的新方法

为了用GPS获得高精度的定位结果，周跳必须在数据处理过程中被检测和修复。通过对周跳产生原因和特点的分析，提出神经网络方法，并与传统方法进行比较。用神经网络建立模型对周跳进行预测。通过对比预测值的不同来确定周跳的大小，从而实现周跳的修复。此外，还利用实测的相位数据，验证方法的可行性与有效性。

New method of detecting and repairing GPS cycle slips based on neural networks

In order to attain high precision positioning and navigation results with GPS,cycle slips must be correctly detected and repaired at the data processing stage.Based on the characteristics of cycle slip analyzed,a neural network method is developed.Model based on artificial neural network was established to detect the cycle slips.The actual number of cycle slip can be determined through comparing the difference of forecasting data of the prediction models.Finally,test results are presented to demonstrate the feasibility and validity of the method.