In the project of upgrading the Guangxi CORS(GXCORS) Beidou Ground-Based Augmentation System, Guangxi Bureau of Surveying, Mapping and Geoinformation, had completed the examination for the instrument of multiple producers about the Compass ground-based augmentation system. The contents of the tests contain the network RTK positioning accuracy, the static processing accuracy, the time availability, the space availability, the environmental availability, etc.. through analyzing the test data, in this paper, drawing some conclusions that reflect the current situation about the Compass Ground-based Augmentation System objectively, it is benefit for the construction and development of the Compass Ground-based Augmentation System.
Combines the wavelet analysis and neural network, this paper will be processed the data and the traditional BP neural network and kalman filter are analyzed and compared. First of all to obtain data of dam deformation wavelet denoising, excluding the contaminated data, obtain the optimal data set. Threshold denoising is generally adopted. Then based on the BP neural network, wavelet analysis to improve the traditional neural network model. Improve the underlying layer upon layer number and the number of nodes. Combined with the optimized dam deformation data, using the improved network model, the results to the regression model, ordinary kalman filter, this paper compares and analyzes the prediction effect evaluation.Comparison result is more ideal, which indicates that the combination of wavelet neural network model for deformation data processing has a good precision.