臺灣博碩士論文加值系統

擴展型卡爾曼(Extended Kalman Filter, EKF)是一種重要消除全球定位系統(GPS)動態定位的隨機誤差的方法。有一種方法稱為自適應漸消卡爾曼濾波器(Adaptive Fading Kalman Filter, AFKF),它利用次佳化漸消因子(Fading Factor,λ)去限制EKF的記憶長度。本論文中我們利用比例因子(Scaling Factor,α)去調整漸消因子λ以加強追蹤性能。
傳統上選擇比例因子十分依賴個人經驗或電腦模擬。為了改進這項缺失，本論文提出命名為模糊自適應漸消卡爾曼濾波器(Fuzzy Adaptive Fading Kalman Filter, FAFKF)這個改進方法。
FAFKF 結合了AFKF以及模糊邏輯自適應系統(Fuzzy Logic Adaptive System, FLAS)，FLAS利用模糊推論系統(Fuzzy Reasoning System, FRS) 與虛擬距離殘差均值與方差的Degree of divergence (DOD)參數，來動態調整比例因子α以更加符合載體實際的動態。
本論文應用FAFKF於全球定位系統 (Global Position System, GPS)導航系統，使其有更佳的定位性能，將與EKF以及AFKF做追蹤性能上的評估比較。

The extended Kalman Filter (EKF) is an important method for eliminating stochastic errors of dynamic position in the Global Positioning System (GPS). One of the adaptive methods is called the Adaptive Fading Kalman filter (AFKF), which employs suboptimal multiple fading factors for limiting the length of memory in an EKF. A scaling factor α has been proposed for increasing the fading factors so as to improve the tracking capability. Traditional approach for selecting the scaling factor α heavily relies on personal experience or computer simulation. In order to resolve this shortcoming, a novel scheme called the fuzzy adaptive fading Kalman filter (FAFKF) is carried out. In the FAFKF, the fuzzy logic reasoning system is incorporated into the adaptive fading Kalman filter. By monitoring the degree of divergence (DOD) parameters based on the innovation information, the fuzzy logic adaptive system (FLAS) is designed for dynamically adjusting the scaling factor according to the change in vehicle dynamics. GPS navigation processing using the FAFKF will be simulated to validate the effectiveness of the proposed strategy. The performance of the proposed scheme will be assessed and compared to those of conventional EKF and AFKF.

誌謝……………………………………………………………………………………i
摘要…………………………………………………………………………………...ii
Abstract………………………………………………………………………...……iii
Table of Contents…………………………………………………………………....iv
List of Figures…………………………………………………………………….….vi
List of Tables………………………………………………………………………..viii

Chapter 1 Introduction………………………………………………………………1
§1.1.General………………………………………………………………….… 1
§1.2 Research Motivation and Method………………………...……………...3
§1.3 Thesis Outline…………………………………………………………...…4

Chapter 2 GPS Navigation processing………………………………………...……6

Chapter 3 GPS Navigation Processing Using The Extend Kalman Filter….…….9

Chapter 4 The Fuzzy adaptive fading Kalman Filter…………………………….14
§4.1 Adaptive fading Kalman filter(AFKF)....................................................15
§4.2 The fuzzy logic adaptive system(FLAS)…………………………..……18
§4.3 Fuzzy adaptive fading Kalman filter (FAFKF)......................................20

Chapter 5 Simulation Experiments…………………………………..…………...24
§5.1 Structure and Settings………….………………………………………..24
§5.2 Simulation and analysis………………………………………………….34
§5.2.1 Results from the Extended Kalman Filter…….…………...................34
§5.2.2 Results from the Adaptive Fading Kalman Filter…………….…….36
§5.2.3 Results from the Fuzzy Adaptive Fading Kalman Filter…………...39
§5.2.4 Comparison and Analysis…………………………………………….41
§5.2.5 Numerical Experimental Results……………………….…………....47
§5.2.6Conclusion…………………….………………………………………..50

References…………………………………………………………………………..52

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