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標題: Shipborne gravity estimation from the combination of inertial measurement unit(IMU) and global positioning system (GPS)
作者: 王偉龍
Wei-Lung Wang
關鍵字: 慣性測量元件;全球定位系統;濾波;重力;IMU;GPS;filter;gravity
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本研究利用船載慣性測量元件(Inertial Measurement Unit, IMU)及全球定位系統(Global Positioning System, GPS)資料進行估算重力值,並進行嚴密的精度分析。IMU與GPS資料來源為國土測繪中心於2011年辦理的「臺灣本島近岸船載重力測量」計畫。資料前處理過程中,考慮各種濾波方式與不同的罩窗寬度組合,以濾波後IMU與GPS的相關係數與差值標準偏差,來決定最佳的濾波方式。計算重力時,再以高斯濾波消除雜訊,以期獲得最佳之重力值。最終重力結果與ZLS船載重力儀和EGM08比較並分析其精度。
研究結果顯示(1)高斯濾波與餘弦濾波對消除雜訊有較佳的結果。(2)航線0514c的IMU與 GPS初始資料,分別在高斯濾波罩窗寬度5.5秒與2秒時,有最高相關係數值0.88。 (3) 航線0519a的IMU與 GPS初始資料,分別在高斯濾波罩窗寬度5.0秒與1秒時,也有最高相關係數值0.88。(4)本研究重力值最佳精度在航線2-2,在使用濾波罩窗寬度200 ? 500秒的精度約為40 ? 50 mgal,而在使用濾波罩窗寬度1000? 1500秒則可達到約20 ? 30 mgal。

The study is aimed at gravity estimation by using Inertial Measurement Unit(IMU) and Global Positioning System(GPS) data, and the accuracies of results are rigorously analyzed. The IMU and GPS data are from the proposal 「Shipborne gravity survey over the inshore areas of Taiwan」 sponsored by National Land Surveying and Mapping Center (NLSC) in 2011. In the step of data pre-processing, we consider different filter techniques and widths to determine a best filter combination according to the correlation coefficients and standard deviations between GPS and IMU data. Gaussian filter is adopted again to eliminate data noises in the step of gravity computation. The results are evaluated both by ZLS ship-derived and by EGM08-derived gravity.
We conclude that (1) Gaussian and Cosine arch filters both exhibit more excellent results than others. (2) In Route 0514c, the best correlation coefficient 0.88 are occurred when the Gaussian filter widths use 5.5 s for IMU, and 2 s for GPS. (3) In Route 0519a, the best correlation coefficient 0.88 are occurred when the Gaussian filter widths use 5.0 and 1 s for IMU and GPS, respectively. (4) Route 2-2 show the best result that the accuracies reach 40 ? 50 mgal at Gaussian filter widths 200 ? 500s, and reach 20 ? 30 mgal at 1000 ? 1500 s.
其他識別: U0005-2811201416184646
Rights: 同意授權瀏覽/列印電子全文服務,2016-08-31起公開。
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