The Multi-antenna GPS Monitoring System Based On The Net And The Study On The Arithmetic Of Dynamic Data Process

Deformation exists widely in the nature and the best way to get the deformation information is deformation monitoring. The application of GPS in deformation monitoring can not only achieves high precision but also has some advantages such as high automatic and no weather condition etc that traditional methods doesn't have. However, the application of GPS in slop and dam deformation monitoring needs much more GPS receivers so as to obtain the full and continuous deformation, thus the deformation cost will increase sharply because of GPS receiver's being expensive. The multi-antenna GPS monitoring system is based on the merits of GPS technology and is designed to greatly reduce the monitoring costs. To realize the real-time and remote automatic monitoring and to forecast deformation trend accurately and timely, the multi-antenna GPS monitoring system should have the capabilities of real-time data communication and the dynamic data processing. Accordingly, this dissertation studies the data communication of the multi-antenna GPS monitoring system, as well as how to use Kalman filter and time series to realize the dynamic data processing and consequently forecast the deformation body safety condition.With this topic, the main works and contributions of this dissertation are summarized as follows:(1) Based on the studying of the basic knowledge of the serial port, one set of GPS data real time communication software is designed, which can not only realize real time data communication, but also can check its correctness and decode it, and consequently write it into the Rinex format.(2) One decoding method based directly on byte data is presented. It avoids some intermediate links and bears the advantages of being simple and great efficiency as well as lesser code and so on.(3) Time series analys is systematically analyzed and is put into practice. The result shows that time series analysis is one simple and efficient method for dynamic data processing. Moreover, a high fitting precision and a better short-term forecast effect can be obtained. (4) Kalman filter and adptive Kalman filter are applied to time series analysis so that the parameters of time series model can be timely updated according to the system inputs.The result suggests that a better fitting and forecast precision can be achieved through time series analysis based on Kalman filter and adptive Kalman filter.