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系統識別號 U0026-0409201910290800
論文名稱(中文) 基於適應性緊耦合INS/GNSS整合系統之優化策略效益分析
論文名稱(英文) The Performance Analysis of Enhancing Strategies Based on Adaptive Tightly-coupled INS/GNSS Integration System
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 107
學期 2
出版年 108
研究生(中文) 田侑騏
研究生(英文) Yu-Chi Tien
學號 P66064162
學位類別 碩士
語文別 英文
論文頁數 81頁
口試委員 指導教授-江凱偉
口試委員-詹劭勳
口試委員-陳國華
中文關鍵字 INS/GNSS整合系統  適應性卡曼濾波器  緊耦合架構  輪速計  氣壓計 
英文關鍵字 INS/GNSS integration system  Adaptive Kalman Filter  Tightly-coupled  Odometer  Barometer 
學科別分類
中文摘要 INS/GNSS整合式導航系統結合傳統的慣性導航系統(Inertial Navigation System, INS)與全球導航衛星系統(Global Navigation Satellite System, GNSS),將兩系統彼此互補的特性整合,以克服慣性導航系統之快速誤差累積導致其不利於長時間應用,以及該系統缺乏絕對位置資訊等資訊,以全球導航衛星系統提供精確且不隨時間累積誤差之定位成果約制誤差累積,同時能夠以高輸出頻率提供長期且可靠的導航成果(位置、速度以及姿態角),因此,整合式導航系統在近年來快速發展並且被廣泛運與在車載導航領域。然而,隨著現代都市區域的迅速發展,大部分的都市環境高樓林立,高架道路時而可見,嚴重影響衛星訊號的接收成效,多數衛星訊號遭受遮蔽,干擾及反射等,其中又以多路徑效應(multipath)與非直視性(Non light-of-sight)的影響最為明顯;傳統且最常見的鬆耦合式整合架構(Loosely-coupled)受限於都市環境之透空度、可視衛星數量等條件時常缺乏GNSS成果,而導致誤差快速累積,而緊耦合式整合架構(Tightly-coupled) 則直接使用原始衛星觀測量,不受限於前述不利的條件。
車載領域中發展出許多輔助手法,用以更近一步提升整體整合式導航系統的穩健度以及導航成果之精度,如加裝輪速計、氣壓計提供額外導航資訊,或利用車載系統的特性進行零速更新(Zero Update)與非和諧約制(non-Holonomic constraint)等;此外,針對整合架構的改良亦可加強核心演算法之穩定度,如利用適應性卡曼濾波器(Adaptive Kalman Filter, AKF)取代常見拓展式卡曼濾波器(Extended KF, EKF),加強導航系統對惡劣都市環境的適應能力。
綜觀前述,本研究以適應性緊耦合INS/GNSS整合系統作為基礎,結合輪速計、氣壓計等之優化策略,搭配實際車載實驗,透過實驗成果以驗證本研究所提出之優化策略於惡劣都市環境之成效。
英文摘要 Inertial Navigation System/Global Navigation Satellite System (INS/GNSS) integration system have been widely applied in recent years. The integration system combines the complementary characteristics of INS and GNSS, and provides precise and continuous navigation solution (position, velocity and attitude information) with high output rate for long term. In addition, it is able to overcome the drawbacks of individual system. Therefore, INS/GNSS integration not only promotes the quality and performance of navigation but also increases the reliability
However, the integration system sometimes malfunctions and the performance heavily deteriorates, especially in urban area where signals from satellites may be blocked or reflected by modern buildings. In multipath or Non Light-of-sight (NLOS) environment, incorrect signal results in poor observability in Kalman Filter (KF). Loosely-coupled (LC) integration scheme has been the most popular strategy due to its simplicity structure, easy feasibility and less computation burden. However, the fatal weakness of LC is that outage of GNSS solution makes it an INS stand-alone system, which troubles LC when in the harsh urban environments and make it unsuitable for future application. On the other hand, Tightly-coupled (TC) exploits the raw measurement directly, which means it keeps on-line calibrating INS error as long as any raw measurement is available.
Besides modifying the core of integration system, other method to enhance the stability of navigator are developed in land navigation field. Vehicular constraints such as Zero Update and Non-Holonomic constraint provide other information based on vehicle motion; aiding sensor, odometer and barometer offers velocity and height, respectively. And Adaptive scheme on KF enhance the ability to adjust itself based on the condition of environment.
Based on those mention previously, an adaptive strategy-based tightly-coupled INS/GNSS integration system aided by odometer and barometer for integrating low-cost MEMS sensors and GNSS for seamless land vehicular application is proposed in this study. For the system performance assessment and evaluation, field tests are conducted with many urban scenarios.
論文目次 摘要 II
ABSTRACT III
誌謝 V
CONTENT VII
LIST OF TABLES IX
LIST OF FIGURES X
CHAPTER 1 INTRODUCTION 1
1.1 BACKGROUND 1
1.2 METHODOLOGY 5
1.3 THESIS OUTLINE 6
CHAPTER 2 POSITIONING RELATED TECHNOLOGY 8
2.1 REFERENCE FRAME 8
2.1.1 INERTIAL FRAME 8
2.1.2 ECEF FRAME 9
2.1.3 NAVIGATION FRAME 10
2.1.4 VEHICLE FRAME 11
2.1.5 SENSOR FRAME 12
2.1.6 TRANSFORMATION BETWEEN FRAMES 12
2.2 INERTIAL NAVIGATION SYSTEM 15
2.2.1 INERTIAL SENSOR ERROR 15
2.2.2 INS MECHANIZATION 17
2.3 GLOBAL NAVIGATION SATELLITE SYSTEM 21
2.3.1 MEASUREMENT MODEL 22
2.3.2 ERROR MODEL 26
CHAPTER 3 INTEGRATION SCHEME 30
3.1.1 LOOSELY-COUPLED SCHEME 31
3.1.2 TIGHTLY-COUPLED SCHEME 32
3.1.3 DEEPLY-COUPLED SCHEME 33
3.2 EXTENDED KALMAN FILTER 35
3.2.1 SYSTEM MODEL 39
3.2.2 MEASUREMENT MODEL 45
CHAPTER 4 ADVANCED INTEGRATION ENHANCEMENT 48
4.1 VEHICULAR CONSTRAINTS 48
4.1.1 ZERO VELOCITY UPDATE (ZUPT) 48
4.1.2 ZERO INTEGRATED HEADING RATE (ZIHR) 49
4.1.3 NON-HOLONOMIC CONSTRAINT (NHC) 49
4.2 ADAPTIVE STRATEGY 51
4.3 AIDING SENSOR 59
4.3.1 ODOMETER 60
4.3.2 BAROMETER 62
CHAPTER 5 EXPERIMENT AND ANALYSIS 67
5.1 SET UP 67
5.2 PERFORMANCE ANALYSIS 70
5.2.1 ADAPTIVE SCHEME 71
5.2.2 ODOMETER AIDING 72
5.2.3 BAROMETER AIDING 74
CHAPTER 6 CONCLUSION 76
6.1 CONCLUSION 76
6.2 FUTURE WORK 77
REFERENCE 78
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