進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-2007201811023000
論文名稱(中文) 利用適應性卡曼濾波器結合車載與垂直約制發展INS/GNSS 整合架構之表現分析
論文名稱(英文) The Performance Analysis of an AKF-based INS/GNSS Integration Scheme with Vehicular and Vertical Constraints in Urban Environment
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 2
出版年 107
研究生(中文) 陳侑良
研究生(英文) You-Liang Chen
學號 P66044023
學位類別 碩士
語文別 英文
論文頁數 120頁
口試委員 口試委員-卓大靖
口試委員-詹劭勳
指導教授-江凱偉
指導教授-張秀雯
中文關鍵字 INS/GNSS整合系統  氣壓計  適應性卡曼濾波器 
英文關鍵字 INS/GNSS  Barometer  Adaptive Kalman Filter 
學科別分類
中文摘要 隨著科技日新月異,全球衛星導航系統(GNSS)與慣性導航系統(INS)被廣泛地應用於導航用途。藉由INS/GNSS整合系統可互補單一系統獨立運行之缺點,進而提升更高精度的定位定向能力。GNSS的優勢在於提供高精確和高精準的位置資訊,但是GNSS容易因為衛星能見度降低而降低精確度和精準度。然而,INS不受外在環境影響可以持續提供加速度及角速度觀測量計算相對位置,可是INS的精準度容易受到誤差和雜訊影響,隨著時間而降低精確度。整合系統的運作讓INS使用整合後的加速度及角速度觀測量提供相對位置,GNSS則是提供初始值和位置更新至整合演算法中,降低INS隨時間而累積的誤差,倘若GNSS發生訊號失鎖,INS則可持續提供導航定位解。
在INS/GNSS整合演算法中,擴展式卡曼濾波器(EKF)最為常見,現今常見的整合架構為鬆耦合(LC);在LC架構中,GNSS濾波器估算出GNSS位置及速度解,更新至INS濾波器中進行最後估算,產生導航定位解,鬆耦合架構簡單且容易實現,但GNSS濾波器在可視衛星低於4顆的情形下,無法提供的位置及速度解,導致INS獨立運作。
在INS/GNSS整合系統中,速度和姿態也相當容易影響定位的準確度。在車載導航系統中,車體正常行駛於路面上不易產生上下震動或左右滑動情形,故車體速度僅有行進方向(X軸)之速度,而垂直於行進方向的Y軸和Z軸速度可約制為零,形成非諧和約制(NHC)。藉由車載移動特性的約制,採取零速更新(ZUPT)、航向約制(ZIHR)和NHC可以降低速度和姿態的誤差,讓定位成果更佳。另外,INS/GNSS整合演算法的高度精度往往比平面精度還差;藉由使用氣壓計可以將氣壓換算為以海平面高度為基準的高度資訊,因此氣壓計可以用來提供高度方向的約制提升定位成果。本研究亦採用適應性卡曼濾波器(AKF)做為核心演算法。在EKF中,無論是觀測量模型或是動態模型,基本上是按照某些特定的假設進行計算,使得EKF的成果僅達成次佳化。AKF之主要目的為調整觀測量的協變方矩陣(R)或是系統模型的協變方矩陣(Q)。在本研究中,主要針對R進行調整。
為了驗證本研究提出之鬆耦合架構的INS/GNSS整合演算法搭配車載約制和高度約制,實驗場景設定於容易產生GNSS多路徑效益或是衛星幾何偏差的都市區域。透過實驗成果顯示,本研究提出的架構相較於原始的卡曼濾波器可提升55%的濾波解的定位精度和35%的平滑解的定位精度。
英文摘要 Global Navigation Satellite Systems (GNSS) integrated with Inertial Navigation System (INS) are widely applied to improve the reliability for navigation. Global Navigation Satellite Systems (GNSS) integrated with Inertial Navigation System (INS) has the complementary characteristics to overcome the drawbacks for each sensor so that the integrated system provides superior performance. The advantage of GNSS is the higher positioning accuracy, but it decreases easily with the worse light-of-sight visibility to the satellites. On the other hand, the benefit of INS is self-contained and independency of external signal. Nevertheless, the accuracy of INS degrades rapidly because of the nonlinear error and noises from inertial sensors including accelerometers and gyros. GNSS is usually used to update the estimates from INS as well as minimize the drifts of inertial measurements over time. Most importantly, the INS bridges the gap of losing GNSS signals in harsh environments such as tunnel, urban area and indoor parking. It is common to use Extended Kalman Filter (EKF) to fuse the heterogeneous data, and loosely-coupled (LC) integration is a simpler GNSS/INS architecture which has two EKF algorithms. However, the output of the first EKF in GNSS will stop functioning when the number of the satellites in view is less than four. Then, the errors in the position and velocity solutions provided by the first EKF in GNSS are time-correlated, which might cause the instability of the second EKF for navigation.
The positioning error of INS/GNSS integration is also influenced by velocity error and attitude error. The motion of the land vehicle will not jump of the ground or slide on the ground under normal circumstances. Thus, the specific vehicular motions become the constraints for the land vehicle navigation. In this research, zero velocity update (ZUPT), zero integrated heading rate (ZIHR) and non-holonomic constraint (NHC) are evaluated for land vehicle application. Furthermore, it is well known that the accuracy of height from INS/GNSS is weaker then horizontal positions; therefore, barometer which estimates the height above the sea level based on the measurement of atmospheric pressure is commonly used for improving the height accuracy. By using barometer, the height constraint is added to improve the accuracy of INS/GNSS integration.
Besides, a LC INS/GNSS integration scheme using Adaptive Kalman Filter (AKF) as the core estimator are implemented in this research. Due to the priori uncertainty from the measurement or the dynamic model, AKF has the capability to reduce the fault caused by the suboptimal of EKF. The significant task for AKF is the tuning algorithm of the measurement covariance matrix (R) or the dynamic model covariance matrix (Q) adaptively. In this study, the innovation-based and residual-based adaptive estimations of the measurement matrix are used for the improvement of Kalman filter.
In order to validate the performance of LC INS/GNSS integration scheme with AKF and EKF, the experimental scenarios are conducted in downtown area where multipath signal is severe or the satellite geometry is bad. The test and reference platform, low-tactical grade INS and high-tactical grade INS, together with the geodetic GNSS antenna/receiver and barometer were mounted on the top of a land vehicle. Analysing the performance of AKF with the adaptive measurement covariance matrix is focused on this research. In addition, not only the vertical constraint from barometer but also the velocity constraints from vehicle are added into AKF. The proposed integration scheme can provide more stable solutions with the vertical and velocity constraints. The results display around 55% / 35% improvements of maximum errors for three dimensional filtered/smoothed positioning errors in the average cases.
論文目次 中文摘要 I
Abstract II
Acknowledgement IV
Contents VI
List of Tables IX
List of Figure XI
Chapter 1 Introduction 1
1.1 Background 1
1.2 Motivation and Objectives 3
1.3 Thesis Outline 5
Chapter 2 INS/GNSS Integrated System 7
2.1 Coordinate Frames and Transformations 7
2.1.1 Inertial frame 7
2.1.2 Earth-Centered-Earth-Fixed frame (ECEF) 8
2.1.3 Navigation Frame 10
2.1.4 Vehicle Frame 10
2.1.5 Body Frame 11
2.1.6 Transformation between Frames 11
2.2 Inertial Navigation System (INS) 13
2.2.1 Inertial Navigation Equations 13
2.2.2 Inertial Sensor Error Model 17
2.3 Global Navigation Satellite Systems (GNSS) 19
2.3.1 GNSS Positioning 19
2.3.2 Error Sources of GNSS 21
2.4 INS/GNSS Integration 22
2.4.1 Integration System Scheme 23
2.4.2 Kalman Filter for INS/GNSS Integration 24
2.4.3 INS Error Model 28
2.4.4 Stochastic Modeling 33
2.4.5 Smoothing 38
Chapter 3 The Improved Approaches for INS/GNSS Integration 42
3.1 Vehicular Constraints 42
3.1.1 Zero Velocity Update (ZUPT) 42
3.1.2 Zero Integrated Heading Rate (ZIHR) Measurement 44
3.1.3 Non-Holonomic Constraint (NHC) 45
3.2 Vertical Constraints 46
3.3 Adaptive Kalman Filter (AKF) 50
3.3.1 Multiple Model Adaptive Estimation (MMAE) 51
3.3.2 Innovation-based Adaptive Estimation (IAE) 53
3.3.3 Residual-based Adaptive Estimation (RAE) 54
3.3.4 Innovation-based Adaptive Scaling Estimation (IASE) 55
3.3.5 Residual-based Adaptive Scaling Estimation (RASE) 58
Chapter 4 Experiments and Analysis 59
4.1 System and Experimental Scenarios Description 59
4.1.1 System Descriptions 59
4.1.2 Experimental Scenarios Descriptions and EKF Performances 61
4.2 The Performance Analysis of Vehicular and Vertical Constraints 67
4.2.1 The Performance in ZUPT/ZIHR-Aided Mode 67
4.2.2 The Performance of NHC 70
4.2.3 The Performance of Vertical Constraint 72
4.3 The Accuracy Analysis between EKF and AKF 87
4.3.1 Urban Scenario-I 87
4.3.2 Urban Scenario-II 91
4.3.3 Urban Scenario-III 95
4.3.4 Urban Scenario-IV 101
4.4 The Performance Analysis of Proposed INS/GNSS Integrated Algorithms 107
Chapter 5 Conclusions and Future Works 113
5.1 Conclusions 113
5.2 Future Works 114
References 117
參考文獻 Aggarwal, P., Syed, Z., Noureldin, A., El-Sheimy, N., 2010. MEMS-Based Integrated Navigation. Artech House.
Almagbile, A., Wang, J., Ding, W., 2010. Evaluating the performances of adaptive Kalman filter methods in GPS/INS integration. Journal of Global Positioning Systems 9, 33-40.
Arulampalam, M.S., Maskell, S., Gordon, N., Clapp, T., 2002. A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking. IEEE Transactions on signal processing 50, 174-188.
Britting, K.R., 1971. Inertial navigation systems analysis.
Brown, R.G., Hwang, P.Y., 1997. Introduction to random signals and applied kalman filtering. NY John Wiley and Sons.
Chiang, K.W., 2004. INS/GPS Integration Using Neural Networks for Land Vehicular Navigation Applications, Department of Geomatics Engineering. University of Calgary, Calgary,Canada.
Chiang, K.W., Huang, Y.W., 2008. An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications. Applied Soft Computing 8, 722-733.
Chiang, K.W., Noureldin, A., El-Sheimy, N., 2004. A new weight updating method for INS/GPS integration architectures based on neural networks. Measurement Science and Technology 15, 2053.
Dimirovski, G.M., 2016. Complex Systems: Relationships between Control, Communications and Computing. Springer.
Duong, T., 2013. Integration Strategies and Estimation algorithms to Improve the Navigation Accuracy of Land-Based Mobile Mapping System, Department of Geomatics. National Cheng Kung University, Tainan, Taiwan.
Farrell, J., Barth, M., 1998. The global positioning system and inertial navigation. McGraw-Hill Professional.
Farrell, J.L., 2007. GNSS Aided Navigation & Tracking: Inertially Augmented Or Autonomous. American Literary Press.
Gelb, A., 1974. Applied optimal estimation. MIT press.
Gleason, S., Gebre-Egziabher, D., 2009. GNSS applications and methods. Artech House.
Godha, S., 2006. Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application, Department of Geomatics Engineering. The University of Calgary, Library and Archives Canada= Bibliothèque et Archives Canada.
Gordon, N.J., Salmond, D.J., Smith, A.F., 1993. Novel approach to nonlinear/non-Gaussian Bayesian state estimation, IEE Proceedings F (Radar and Signal Processing). IET, pp. 107-113.
Grewal, M., Andrews, A., 2008. Kalman Filtering. Hoboken. NJ: John Wiley and Sons.
Grewal, M.S., Weill, L.R., Andrews, A.P., 2001. Global Positioning System, Inertial Navigation and Integration. . A John Wiley and Sons Inc. .
Hajiyev, C., Soken, H.E., Vural, S.Y., 2016. State estimation and control for low-cost unmanned aerial vehicles. Springer.
Hayal, A.G., 2010. Static calibration of the tactical grade inertial measurement units, Department of Geodetic Science. The Ohio State University.
Hou, H., 2004. Modeling inertial sensors errors using Allan variance, Department of Geomatics Engineering. University of Calgary.
Kaplan, E., Hegarty, C., 2005. Understanding GPS: principles and applications. Artech house.
Kayton, M., Fried, W.R., 1997. Avionics navigation systems. John Wiley & Sons.
Kim, Y., Kim, J., Yu, S., Kee, C., Park, B., 2016. Multipath mitigation using GPS/INS integrated navigation with adaptive Kalman Filtering, 2016 International Technical Meeting of The Institute of Navigation, Monterey, California, pp. 893 - 901.
Lee, D., Park, K.W., Park, C., Kang, I.-S., 2015. An efficient heave estimation using Time-Differenced GPS carrier phase measurements and compensated barometer measurement applying error model, Navigation World Congress (IAIN), 2015 International Association of Institutes of. IEEE, pp. 1-6.
Liao, J.K., Zhou, Z.M., Tsai, G.J., Duong, T.T., Chiang, K.W., 2014. The Applicability Analysis of Using Smart Phones for Indoor Mobile Mapping Applications, The 27th International Technical Meeting of The Satellite Division of the Institute of Navigation (ION GNSS+ 2014), Tampa, Florida, pp. 510-531.
Liu, C.Y., 2012. The Performance Evaluation of a Real-time Low-cost MEMS INS/GPS Integrated Navigator with Special Aiding Algorithms for Land Applications, Department of Geomatics. National Cheng Kung University, Tainan, Taiwan.
Magill, D., 1965. Optimal adaptive estimation of sampled stochastic processes. IEEE Transactions on Automatic Control 10, 434-439.
Maybeck, P.S., 1982. Stochastic models, estimation, and control. Academic press.
Mehra, R., 1971. On-line identification of linear dynamic systems with applications to Kalman filtering. IEEE Transactions on Automatic Control 16, 12-21.
Mohamed, A., Schwarz, K., 1999. Adaptive Kalman filtering for INS/GPS. Journal of geodesy 73, 193-203.
Mohamed, A.H., 1999. Optimizing the estimation procedure in INS/GPS integration for kinematic applications. Department of Geomatics Engineering. University of Calgary, Calgary,Canada.
Nassar, S., Niu, X., El-Sheimy, N., 2007. Land-vehicle INS/GPS accurate positioning during GPS signal blockage periods. Journal of Surveying Engineering 133, 134-143.
Nassar, S., Syed, Z., Niu, X., El-Sheimy, N., 2006. Improving MEMS IMU/GPS systems for accurate land-based navigation applications, ION NTM, pp. 523-529.
Park, J., Lee, D., Park, C., 2015. Implementation of Vehicle Navigation System using GNSS, INS, Odometer and Barometer. Journal of Positioning, Navigation, and Timing 4, 141-150.
Peng, K.Y., Lin, C.A., Chiang, K.W., 2012. The Performance Analysis of AN Akf Based Tightly-Coupled Ins/gps Integrated Positioning and Orientation Scheme with Odometer and Non-Holonomic Constraints. ISPRS-International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 1, 481-486.
Petovello, M.G., 2004. Real-time integration of a tactical-grade IMU and GPS for high-accuracy positioning and navigation, Department of Geomatics Engineering. University of Calgary, Library and Archives Canada= Bibliothèque et Archives Canada.
Ristic, B., Arulampalam, S., Gordon, N.J., 2004. Beyond the Kalman filter: Particle filters for tracking applications. Artech house.
Rogers, R.M., 2000. Applied mathematics in integrated navigation systems. AiAA.
Shin, E.H., 2001. Accuarcy improvement of low cost INS/GPS for land applications. University of Calgary.
Shin, E.H., 2005. Estimation techniques for low-cost inertial navigation, Department of Geomatics Engineering. University of Calgary, Library and Archives Canada= Bibliothèque et Archives Canada.
Sokolovic, V.S., Dikic, G., Stancic, R., 2013. Integration of INS, GPS, magnetometer and barometer for improving accuracy navigation of the vehicle. Defence Science Journal 63, 451-455.
Sukkarieh, S., 2000. Low cost, high integrity, aided inertial navigation systems for autonomous land vehicles. Australian Center for Fields Robobtics, University of Sydney. Sydney, Australia.
Tanigawa, M., Luinge, H., Schipper, L., Slycke, P., 2008. Drift-free dynamic height sensor using MEMS IMU aided by MEMS pressure sensor, Positioning, Navigation and Communication, 2008. WPNC 2008. 5th Workshop on. IEEE, pp. 191-196.
Titterton, D., Weston, J.L., 2004. Strapdown inertial navigation technology. IET.
Wang, J., Stewart, M., Tsakiri, M., 2000. Adaptive Kalman filtering for integration of GPS with GLONASS and INS, Geodesy Beyond 2000. Springer, pp. 325-330.
White, N.A., 1996. MMAE Detection of Interference/Jamming and Spoofing in a DGPS-Aided INS. MS thesis, AFIT/GE/ENG/96D-21, School of Engineering, Air Force Institute of Technology, Wright-Patterson AFB, OH.
Yao, G.Y., 2010. The Performance Analysis of an AKF Based Tightly Coupled INS/GNSS Sensor Fusion Scheme with Non-holonomic Constraints, Department of Geomatics. National Cheng Kung University.
Yen, S.W., van Graas, F., de Haag, M.U., 2016. Positioning with two satellites and known receiver clock, barometric pressure and radar elevation. GPS solutions 20, 885-899.
Zhou, Z.M., 2015. The Performance Analysis of Seamless Navigation Systems using Smartphones, Department of Geomatics. National Cheng Kung University.

論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2023-07-10起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2023-07-10起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw