WSEAS TRANSACTIONS on ELECTRONICS
Figure 8. The position error using extension neural network.
Figure 9. Time difference between GPS receiver and satellite by using extension neural network.
This paper proposed using GPS carrier phase measurements to perform frequency stability and time calibration. Originally, position precision was not high and time error was great. Using an extension controller and extension neural network to adjust GPS receiver oscillation frequency will enhance the position precision and frequency stability.
The advantage of the extension controller is that it produces an extension element model for any required control system. The extension controller
the relational function
syntonization and integer ambiguity, then combines the default value of the relational function. The control targets are controlled one at a time, until satisfactory system control is obtained. The experiment results show that the time error is
4 .8 5 × 1 0 − 7
seconds to 2 .8 5 × 1 0 − 8
advantage of the proposed approach is the neural network learning mechanism and
theory element model. The influences positioning accuracy frequency stability of the GPS
factor that most is the oscillation receiver, the time
syntonization, and extension distance
integer ambiguity. The relational and weighting factor adjustment
are merged to adjust non-ideal interval fast and efficiently.
value into an ideal The entire system
performs to obtain
frequency stability and time syntonization the optimum control result. The time error
3 × 10 −8
the extension neural network. precision is greatly promoted.
In the future, we can use the cesium atom clock with higher precision as the foundation for adjusting the receiver oscillation frequency. At the same time, the cesium clock frequency can dispel the multipath effects and estimate the cycle slip error to better improve vehicle position precision. Through wired or wireless transmissions, the proposed method gives all remote stations accurate analysis data. And then the frequency of remote GPS receivers can be syntonized using the extension neural network algorithm. Figure 10 shows an overview of the entire system to achieve frequency stability and time syntonization.
Figure 10. Systematic block diagram of the frequency control process using carrier phase.
Issue 7, Volume 4, July 2007