| Summary: | 碩士 === 中國科技大學 === 土木與防災設計研究所 === 99 === Taiwan is located in the junction of Eurasian Plate and Philippine Sea Plate. So it is with frequent earthquakes and fragile geology. Also during the rainy and typhoon seasons, often cause large-scale rockfall, landslides or other slippery slope disasters. That is a serious threat to the lives and the structures, also is a huge impact to Taiwan. Additionally in the recent years of the world, in the situation without the typhoon and earthquake, the moment of collapse of structures do still cause the casualties of life. It appears the human is lacking for the predict ability and knowledge. Thus the monitoring the relative is to be highlighted importance and necessity.
This study will use building of China University of Technology as experimental subjects. Using e-GPS to establish the GPS reference points B1, B2 and B3 in the school district, and using traditional survey skill to re-measure and analysis the baseline reference point coordinates. Then use the Matlab 7.01 version software and the position simulation program which is developed by China University of Technology structure safety and hazard mitigation center to simulate satellite position signals.
After then, to utilize the Pseudo range (Least Square), Pseudo range (Kalman Filter), Carrier phase (Least Square) and Carrier phase (Kalman Filter) to perform the simulated positioning for the reference points B1, B2 and B3, respectively. According to the comparison the differences and positioning accuracy which are by different positioning satellite signals with different algorithms, The Carrier phase (Kalman Filter) shows the highest positioning accuracy, the average absolute error will be in 2cm range.
In case it can be applied to the structure monitoring in the future, if a regular low-cost vehicle satellite receiver device can be used with the after processing algorithms, to obtain a long-term stability of high-precision positioning, it can reduce the current cost of GPS monitoring, and achieve a long-term, real-time disaster monitoring.
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