臺灣博碩士論文加值系統

摘 要
慣性導航系統(Inertial Navigation System，簡稱INS)之位置和速度的估算誤差會隨著時間而增加。為了改進INS上述問題，通常選擇採用INS加上全球定位系統(Global Positioning System，簡稱GPS)。然而GPS仍然有很多未解決的問題存在，例如觀測環境不佳而無法精確定位之缺點。虛擬衛星(pseudo-satellite，縮寫pseudolite，簡稱PL)是在地面上類似GPS衛星信號之發射，在觀測環境不佳情況下，它提供額外之觀測量，進而提升GPS定位之精度。藉著加入PL以減少行動載具在導航定位上受到精確度降低值(dilution of precision，DOP)的影響。在本研究中，採用PL作為地標台之構想，在載具自動導航定位之動態狀態估計，載具與地標台經建立視線 (Line of Sight，簡稱LOS)直角座標系可量測得距離、方位角與俯仰角。將載具的位置與速度估測值使用混合座標系(修正型球面座標系及直角座標系)之方式進行濾波與預測。使用共變異匹配法(covariance matching method)資料融合技術進行資料融合，以改善定位的精度。模擬結果顯示，在位置部份，資料融合較濾波改善了約87%；在速度部份，資料融合較濾波改善了約76%。本研究提出的演算法在使用兩個地標台於行動載具的導航定位有更精確的估計。

Abstract
Position and velocity errors of Inertial Navigation System (INS) increase unboundedly in time. In order to improve the performance parameters, the alternative of an INS assisted by Global Positioning System (GPS) is frequently adopted. However, GPS still has many unsolved problems existing, for instance the degradation of positioning precision when the view of sky is obstructed. Pseudolite (pseudo-satellite, PL), a ground-based GPS satellite-like signal transmitter, can provide extra measurements in order to improve the positioning precision from GPS alone in the obstructed areas. The pseudolite observations reduced the effect of dilution of precision (DOP) that leads navigation strategy of mobile vehicles. In this research, the pseudolites can be used as landmarks. The problem dealt with here is that of estimating the kinematic state components of a vehicle in autonomous navigation using range, elevation and azimuth angles measured by Line of Sight (LOS) coordinate system. The estimates of the position and velocity of the vehicle are provided by the hybrid coordinate filtering approach (Modified Spherical Coordinates and Cartesian Coordinates Systems). The data fusion algorithm called covariance matching method is employed to reduce the estimation errors. Results of this research show that the Averaged Root Mean Square Error (ARMSE) of position and velocity with the filters was found to be larger (about 87% and 76%) than with the data fusion. Simulation results of proposed algorithm show that the two landmarks may influence the estimating accuracies of mobile vehicle navigation.

目 錄
誌 謝 i
摘 要 ii
Abstract iii
目 錄 iv
圖目錄 vi
表目錄 vii
符號表 viii
符號表 viii
第一章 緒論 1
1.1 研究背景與動機 1
1.2 研究目的 2
1.3 論文架構 3
第二章 文獻探討 5
2.1 無人飛行載具之應用 5
2.2 虛擬衛星 6
2.2.1 虛擬衛星之優點 7
2.2.2 虛擬衛星實際運作考量 9
2.3 全球衛星定位系統(Global Positioning System，GPS) 10
2.4 抵達時間定位法(Time of Arrival，TOA) 10
2.5 混合座標系 11
2.6 系統誤差分析 11
2.6.1全球衛星定位系統 (Global Positioning System，GPS)誤差 11
2.6.2 慣性導航系統(Inertial Navigation System，INS) 誤差 12
2.7卡爾曼濾波演算法 14
2.8 資料融合演算法 15
2.8.1狀態向量資料融合演算法 16
2.8.2.量測資料融合演算法 17
第三章 研究方法 19
3.1 混合座標系動態模型與量測模式 19
3.1.1 MSC狀態與量測 19
3.1.2 量測模式 22
3.1.3 卡氏座標系狀態估計 23

3.2 混合座標系濾波之架構 24
3.3 資料融合演算法 26
第四章 模擬結果與分析 27
4.1使用兩個虛擬衛星地標台定位之S型場景 27
4.2使用兩個虛擬衛星地標台定位之U型場景 32
4.3使用兩個虛擬衛星地標台定位之直線場景 35
4.4模擬結果分析 39
第五章 結論與建議 41
參考文獻 42
附錄 A 45

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