Abstract | Full Paper | PDF | Printer Friendly Format

Page 1 of 7
| Next |

Introduction
Global Positioning System (GPS) is a satellite based radio positioning, navigation and time transfer system developed by the U.S. Department of Defence. The whole GPS constellation consists of 24 satellites allocated in six orbital planes. Each satellite transmits two carrier signals on the L1 (1575.42 MHz) and L2 (1227.6 MHz) frequencies. A receiver tracks these GPS satellites and receives signals containing time and satellites orbit parameters. By using this information, each receiver will be able to calculate its ranges to the visible satellites and correct its clock. Having four or more simultaneous calculated ranges, the receiver can determine its position and precision time [1, 2].

In addition to Selective Availability (SA), there are some other error sources that cause the position and time measuring from GPS receivers to be inaccurate. These significant error sources are signal delays from ionospheric and tropospheric effects, satellite clock drift, satellite orbital position errors, signal multi path, and noise generated within the receiver itself

Because of the above mentioned error sources, all measured GPS positions have a certain number of errors. This means that the received data from a GPS receiver will not determine the exact location. Therefore, users who wish to increase the accuracy of their GPS receivers should take steps to minimize the errors. There are several methods to remove or decrease these errors. One of the most famous is Real Time Differential GPS (DGPS) positioning. In real time DGPS; we need to predict the future (next second) errors. So the prediction or estimation of the future errors is too important [3, 4].

In this research, we described a dynamic predictor which is based on the principle of Kalman filter scheme.