臺灣博碩士論文加值系統

全球導航衛星系統（GNSS）已經由軍方以及平民被廣泛使用。在GPS的應用上不僅需要精確的導航解，也需要是可以絕對放心使用的定位解。衛星導航系統往往會受到多種形式的有意或者無意干擾，導致導航定位性能下降，甚或無法正常工作。欺騙式干擾是發射與GPS訊號除資訊碼不同外，其餘具有相同參數的假訊號干擾GPS接收機，使接收機使用空間中的假訊號進行定位計算，誤導衛星導航接收機偏離準確的導航和定位，不易被發現，具有很好的隱蔽性。因此，欺騙和反欺騙算法已經成為GPS學科內的重要研究課題，抗欺騙技術抑制方法目前以定位/導航解為主。本論文針對抗欺騙式干擾訊號的強健性問題，將M-估測與非線性卡爾曼濾波器相結合，並增加ㄧ檢測機制，使得於未遭受到欺騙式干擾訊號時，運用最佳化估測；若遭受到欺騙式干擾訊號，則啟動M-估測進行強健估測，讓其定位結果既解決了針對運動模型非線性的估測問題，又能較好的對抗欺騙式干擾訊號，大大提升估測的強健性，及定位精度。

The global navigation satellite system (GNSS) has been widely used by militaries as well as civilians. The use of GPS in mission-critical applications demands not only precise navigation solutions but solutions which can be used with absolute confidence. Spoofing is a deliberate interference that aims to coerce global navigation satellite system receivers into generating false position/navigation solutions. The GPS system is generally lack of immunity against spoofing attack. As such, spoofing and anti-spoofing algorithms have become an important research topic within the GPS discipline. Currently, Spoofing mitigation technique of anti-spoofing is mainly on positioning solution. This thesis to solve the problems of spoofing signal of robust estimation, using MEKF and MUKF, which are EKF (Extended Kalman Filter) and UKF (Unscented Kalman Filter) combined with M-estimation. And joining the detection mechanism, so that using optimal estimate when did not suffer spoofing signal; on the other hand, if suffer spoofing signal starting M-estimator and using robust estimate to solve position/navigation solutions. MEKF and MUKF can not only solve the nonlinear problems but also against spoofing signal, which have greatly improved the robust of estimation, and positional accuracy.

誌謝 I
摘要 II
Abstract III
目錄 IV
圖目錄 VI
表目錄 XI
第一章 緒論 1
1.1 前言 1
1.2 研究動機 1
1.3 論文架構 2
第二章 GPS導航系統介紹 3
2.1 GPS導航系統 3
2.1.1 GPS訊號結構 3
2.1.2 GPS接收機架構 4
2.1.3 訊號擷取與追蹤 5
2.2 GPS定位原理 6
第三章 欺騙式干擾(Spoofing) 11
3.1 欺騙式干擾訊號產生 11
3.2 抗欺騙技術(Anti-Spoofing) 11
第四章 卡爾曼濾波器 16
4.1 散型卡爾曼濾波器(The Discrete Kalman Filter) 16
4.2 擴展型卡爾曼濾波器(Extended Kalman Filter，EKF) 18
4.3 無跡卡爾曼濾波器(Unscented Kalman Filter，UKF) 22
4.3.1 UT轉換(Unscented Transformations，UT) 23
4.3.2 無跡卡爾曼濾波器(Unscented Kalman Filter，UKF) 25
第五章 M-估測 28
5.1 M-估測權值分析 28
5.2 快照式(Snapshot approach)抗欺騙式干擾之強健M-估測導航解 29
5.3 基於強健M-估測之抗欺騙式干擾卡爾曼濾波導航解 30
5.3.1 M-估測之擴展性卡爾曼濾波導航演算 31
5.3.2 M-估測之無跡卡爾曼濾波導航演算 32
第六章 結果與分析 33
6.1 實驗模擬概述 33
6.2 檢測機制 37
6.2.1 CASE 1：當第4顆衛星遭受欺騙式干擾訊號 38
6.2.2 CASE 2：當第4.6顆衛星遭受欺騙式干擾訊號 40
6.2.3 CASE 3：當第2.4.6顆衛星遭受欺騙式干擾訊號 43
6.2.4 CASE 4：當第4顆衛星遭受欺騙式干擾訊號 45
6.2.5 CASE 5：當第4.6顆衛星遭受欺騙式干擾訊號 48
6.2.6 CASE 6：當第2.4.6顆衛星遭受欺騙式干擾訊號 50
6.2.7 CASE 7：當第4顆衛星遭受欺騙式干擾訊號 53
6.2.8 CASE 8：當第4.6顆衛星遭受欺騙式干擾訊號 55
6.2.9 CASE 9：當第2.4.6顆衛星遭受欺騙式干擾訊號 58
6.2.10 CASE 10：當第4顆衛星遭受欺騙式干擾訊號 60
6.2.11 CASE 11：當第4.6顆衛星遭受欺騙式干擾訊號 63
6.2.12 CASE 12：當第2.4.6顆衛星遭受欺騙式干擾訊號 65
6.3 強健M-估測於擴展性卡爾曼濾波器之比較 68
6.3.1 CASE 13：當第4顆衛星遭受欺騙式干擾訊號 68
6.3.2 CASE 14：當第4.6顆衛星遭受欺騙式干擾訊號 70
6.3.3 CASE 15：當第2.4.6顆衛星遭受欺騙式干擾訊號 73
6.4 強健M-估測於無跡卡爾曼濾波器之比較 75
6.4.1 CASE 16：當第4顆衛星遭受欺騙式干擾訊號 75
6.4.2 CASE 17：當第4.6顆衛星遭受欺騙式干擾訊號 78
6.4.3 CASE 18：當第2.4.6顆衛星遭受欺騙式干擾訊號 80
6.5 M-估測導航解之比較 83
6.5.1 CASE 19：當第4顆衛星遭受欺騙式干擾訊號 83
6.5.2 CASE 20：當第4.6顆衛星遭受欺騙式干擾訊號 85
6.5.3 CASE 21：當第2.4.6顆衛星遭受欺騙式干擾訊號 88
6.6 Huber M-估測導航解之比較 90
6.6.1 CASE 22：當第4顆衛星遭受欺騙式干擾訊號 90
6.6.2 CASE 23：當第4.6顆衛星遭受欺騙式干擾訊號 93
6.6.3 CASE 24：當第2.4.6顆衛星遭受欺騙式干擾訊號 95
第七章 結論與未來展望 98
7.1 結論 98
7.2 未來展望 100
參考文獻 101

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