An Advanced GPS Carrier Tracking Loop Based on Neural Networks Algorithm

Jichun Shen, Shuai Chen, Changhui Jiang, Yuming Bo

The GPS (Global Positioning Systems) is the main tool to provide the PVT (position, velocity and time) service in our daily life. However, there are some drawbacks needed to be overcome for the wider applications of GPS. It is hard for a popular GPS receiver to position in a high dynamic/weak signal environment without outer aiding. On the basis of analyzing the reason and the error sources of the tracking loop, a neural networks algorithm is used to adjust the parameters of the fusion of the 2rd FLL and 3rd PLL. A judge factor is selected to present the phase errors of the carrier loop. A nonlinear function between a judge factor and fusion parameters is constructed by the neural network algorithm. The method essentially changes the bandwidth of the tracking loop by changing the loop gain, which is usually ignored. The algorithm is implemented and tested in a Matlab software receiver. The experiments show the modified tracking carrier loop can work well in higher dynamic environments compared with standard carrier tracking loop.