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系統識別號 U0026-2112201714451900
論文名稱(中文) 基於手機多感測器整合輔助不定向PDR行人室內導航應用
論文名稱(英文) Pedestrian Indoor Navigation By Mobile Sensors Integration Aided PDR With Arbitrary Orientation
校院名稱 成功大學
系所名稱(中) 測量及空間資訊學系
系所名稱(英) Department of Geomatics
學年度 106
學期 1
出版年 106
研究生(中文) 楊宗効
研究生(英文) Tsung-Hsiao Yang
學號 P66041091
學位類別 碩士
語文別 英文
論文頁數 153頁
口試委員 指導教授-張秀雯
口試委員-江凱偉
口試委員-詹劭勳
中文關鍵字 慣性導航  行人航位推算  室內導航  微機電系統 
英文關鍵字 inertial navigation  pedestrian dead reckoning  indoor navigation  MEMS 
學科別分類
中文摘要 近年來衛星定位系統以及個人攜帶裝置的發展,使得個人導航系統漸漸普及於社會大眾,個人導航系統讓使用者可以方便攜帶裝置於戶外進行導航,使用者在戶外進行定位時,大部分所使用工具來自於GNSS系統,GNSS系統為GPS(美國)、GLONASS(俄羅斯)、BeiDou(中國)以及Galileo(歐盟)等衛星系統的總稱,利用衛星之間傳遞的訊號可讓在地球上的使用者進行個人定位,但是在室內時,因訊號遮蔽、多路徑效應等影響,使得GNSS系統在室內定位時成果不佳,鑒於此現象,人們研究出於室內定位的另外辦法。
如Wi-Fi、藍芽或是Beacon等,都可輔助使用者進行室內定位,然而這些需外部裝置方可使用的技術需要消耗若干的成本與人力布置,再者,使用者使用接收器於佈置後的空間進行定位時可能會有訊號衰減的情況。相對於這幾個主要的缺點,PDR(行人航位推算)並無這些情況。
PDR主要藉由慣性測量元件(加速度計、陀螺儀)來進行定位,由於科技的發展,在近期加入磁力計以及氣壓計,讓使用者不單只是使用原本的感測器進行室內定位。近年來由於MEMS(微機電系統)晶片的發展,使得智慧型手機亦可作為室內定位的工具。PDR依賴陀螺儀或是磁力計給定的方位角計算使用者的定位結果,該情況中使用者手持手機時使用者須固定手機姿態,若隨意水平轉動會使手機的方位角計算與實際行走的方向產生誤差,其誤差計算為該研究的主軸,使用主成分分析消除隨意水平轉動手機產生的誤差,消除後便可得到理想的定位成果。
本研究以主成分分析為主,以其他如:使用兩種感測器整合方位角、擴增卡曼濾波計算口袋型式手機姿態以及類神經探討分析當使用者行走軌跡為三維時這些為研究次要部分,藉由這些對室內定位進行初步的探討以及分析,以期對未來室內定位發展有幫助。
英文摘要 Navigation has been used by civilians for couples of years. Navigation is composed of GNSS and inertial sensors. Two kinds of techniques are used to aid with each other for the outside navigation. The advantage of GNSS is that it can calculate the reference solutions, on the other hand inertial sensors are used to calculate the integrated results which are relative solutions.
When the users are in the buildings the signals will be blocked so that the GNSS solutions would be not accurate. As a result, using inertial sensor doing the indoor positioning is one of the methods whose name is called “Pedestrian Dead Reckoning (PDR)”. To make the resolutions more accurate researchers have been focus on the accuracy of resolutions. Thanks to the improvement of inertial sensors their volumes have become small enough so that they can be installed in portable devices like smartphones. Meanwhile, the smartphones have gradually become one of the important belongings to the civilians. Our research will use smartphones as tools in the PDR experiments.
In PDR research, the pedestrian’s heading is the most important factor for the users. If the heading is not close to the true pedestrian’s heading it will be hard to know the users’ positions exactly. There are various factors which will influence the heading such as sensors’ bias, magnetometer and arbitrary orientation. In here, the arbitrary orientation means rotating sensors on hand horizontally. On the other hand, the judgement of how users take smartphones is also an important topic. There are many different methods used to judge the movements which contain walking-upstairs, walking-downstairs and walking-flat.
The research will mention and talk about three parts, one is about introduction of PDR which contains artificial neural network, another is about smartphone sensor in navigation and its calibration and the other is talking about using certain method to compute true
heading when the platform is rotated.
論文目次 中文摘要…I
ABSTRACT…II
ACKNOWLEDGEMENT…IV
TABLE OF CONTENTS…V
LIST OF TABLES…VII
LIST OF FIGURES…VIII
CHAPTER 1: INTRODUCTION AND OVERVIEW…1
1-1 Background…3
1-2 Motivation…6
1-3 Methodology…9
1-4 Thesis Outline…10
CHAPTER 2: INERTIAL NAVIGATION AND PEDESTRIAN DEAD RECKONING…12
2-1 Reference Frames…13
2-2 Introduction of Inertial Navigation System…15
2-3 Pedestrian Dead Reckoning…24
2-3-1 User Parameter…24
2-3-2 Peak Detection…25
2-3-3 Azimuth (Gyroscope & Magnetometer)…27
2-3-3-1 Gyroscope…28
2-3-3-2 Magnetometer…29
2-4 Motion Recognition (Hand-Held & Pocket…30
2-4-1 Hand-Held…30
2-4-2 Pocket…31
CHAPTER 3: PROBLEM STATEMENTS AND SOLUTIONS…37
3-1 Problem Statements…37
3-2 Current Techniques…38
3-2-1 Low Pass Filter…38
3-2-2 Kalman Filter…40
3-2-2-1 Integration of Azimuth…43
3-2-2-2 Estimation of Attitude Error and Sensor Bias…45
3-2-2-3 Autonomous Calibration of MEMS Gyros in Portable Devices…48
3-2-2-4 Quaternion-Based EKF…53
3-2-3 Magnetometer Calibration…58
3-2-4 Application of Arbitrary Orientation…62
3-2-4-1 Background of Principal Component Analysis (PCA)…62
3-2-4-2 Principal Component Analysis (PCA)…63
3-2-4-3 Compensation of Misalignment From Arbitrary Orientation By PCA…65
3-3 Conclusion of Current Techniques…67
CHAPTER 4: ARTIFICIAL NEURAL NETWORK…69
4-1 Principles of Artificial Neural Network…69
4-1-1 Models of Artificial Neural Network…70
4-1-2 Architectures of Artificial Neural Network…73
4-1-3 Learning Process of Artificial Neural Network…76
4-2 Multi-Layered Feed-Forward Neural Networks…77
CHAPTER 5: EXPERIMENT SETUP…83
5-1 Smartphone’s Specification…83
5-2 Smartphone’s App…84
5-3 Artificial Neural Networks Experiment Setup…85
5-4 Artificial Neural Network Experiment Route And General Experiment Route…90
5-5 Calibration of Magnetometer Experiment…94
5-6 Principal Component Analysis Experiment…95
CHAPTER 6: RESULTS AND ANALYSIS…97
6-1 Gyroscope and Magnetometer based PDR in 2D…97
6-2 Magnetometer Calibration…113
6-3 Gyroscope and Magnetometer based PDR aided by ANN…119
6-4 Quaternion Estimation of Attitude in Pocket Mode…130
6-5 PDR in Arbitrary (Rotate Smartphone Horizontally) Orientation Mode…132
CHAPTER 7: CONCLUSIONS AND FUTURE WORK…135
7-1 Conclusions…135
7-2 Future Work…136
REFERENCE…137
APPENDIX…141
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