隨著自駕車（Autonomous Vehicle，AV）技術持續邁向更高等級的自動化，在實際都市環境中達成高精度與高穩定性的定位能力顯得愈加重要。本論文提出一套具備冗餘設計的 IMU/GNSS 整合導航系統，採用聯邦濾波器架構，結合多顆低成本 MEMS 等級 IMU，以提升在各種真實場景下，包括開闊空間、GNSS 遮蔽與干擾環境中的定位精度與穩健性。本研究分析兩種整合方法：聯邦鬆耦合（Loosely Coupled，LC）與增強聯邦緊耦合（Tightly Coupled，TC）。
相較於僅使用單一 IMU 的傳統鬆耦合架構，該系統透過多 IMU 冗餘設計、聯邦濾波處理與故障檢測排除機制，有效降低 GNSS 失效與慣性漂移對導航表現的影響。實地實驗涵蓋多種都市場景，包括隧道、狹窄巷弄與高樓林立區域。
實驗結果顯示，聯邦 LC 架構已可有效提升定位穩定性，而強化型聯邦 TC 架構則能在多數 GNSS 干擾環境中達到 3D 定位均方根誤差低於 50 公分，接近「車道內定位」所需的導航精度。在 GNSS 完全失效的情境中，系統亦可維持約 1 公尺的 3D RMSE，且相較於 LC 架構可降低超過 40% 的誤差。
總結而言，本研究所提出之系統具備可擴展性、高性價比與良好定位精度，適用於低成本自駕平台，展現其於複雜都市環境中部署之潛力與可行性。

As autonomous vehicle technologies advance, achieving precise and robust positioning in real-world urban environments has become increasingly essential. This thesis proposes a redundant IMU/GNSS integrated navigation system based on a federated filter architecture that utilizes multiple low-cost MEMS IMUs to enhance localization accuracy and reliability across various real-world scenarios, including open-sky, GNSS-denied, and GNSS-challenging conditions. Both proposed federated LC and federated enhanced TC methods are analyzed.
Unlike conventional LC systems with a single IMU, which are vulnerable to GNSS outages and inertial drift, the proposed architecture integrates multi-IMU redundancy, federated filtering, and fault detection and exclusion mechanisms to ensure robust and consistent performance. Field experiments were conducted in challenging urban environments such as tunnels, narrow alleys, and dense high-rise areas.
Results show that the federated LC configuration already improves positioning robustness. Furthermore, the federated enhanced TC method achieves the 3D positiion RMSEs below 50 cm in most GNSS-challenging scenarios, approaching the "Where in Lane" accuracy level. In GNSS-denied environments, it maintains a 3D RMSE approximately 1 meters and achieves over 40% error reduction compared to federated LC.
In summary, this work presents a scalable, affordable, and accurate localization solution suitable for low-cost AV platforms. The system demonstrates strong potential for deployment in complex urban settings, offering high navigation accuracy even under severe GNSS constraints.

Allan, D. W. (1966). Statistics of Atomic Frequency Standards. Proceedings of the IEEE, 54(2), 221–230. https://doi.org/10.1109/PROC.1966.4634
Angrisano, A. (2010). GNSS/INS Integration Methods.
Bancroft, J. B. (2010). Multiple Inertial Measurement Unit Integration for Pedestrian Navigation. The University of Calgary.
Bancroft, J. B., & Lachapelle, G. (2011). Data Fusion Algorithms for Multiple Inertial Measurement Units. Sensors 2011, Vol. 11, Pages 6771-6798, 11(7), 6771–6798. https://doi.org/10.3390/S110706771
Braasch, M. (2022). Fundamentals of inertial navigation systems and aiding. Fundamentals of Inertial Navigation Systems and Aiding, 1–394. https://doi.org/10.1049/sbra550e
Chen, K., Chang, G., & Chen, C. (2021). GINav: a MATLAB-based software for the data processing and analysis of a GNSS/INS integrated navigation system. GPS Solutions, 25(3), 1–7. https://doi.org/10.1007/S10291-021-01144-9/TABLES/2
Cheng, J., Dong, J., Landry, R., & Chen, D. (2014). A Novel Optimal Configuration form Redundant MEMS Inertial Sensors Based on the Orthogonal Rotation Method. Sensors 2014, Vol. 14, Pages 13661-13678, 14(8), 13661–13678. https://doi.org/10.3390/S140813661
Conley, R., Cosentino, R., Christopher J. Hegarty, Elliott D. Kaplan, Joseph L. Leva, Maarten Uijt de Haag, & Karen Van Dyke. (2005). Understanding GPS: Principles and Applications (second edition), Chapter 7: Performance of Stand-Alone GPS (2nd ed.). Artech. https://www.globalspec.com/reference/79920/203279/chapter-7-performance-of-stand-alone-gps
de Alteriis, G., Accardo, D., Conte, C., & Lo Moriello, R. S. (2021). Performance Enhancement of Consumer-Grade MEMS Sensors through Geometrical Redundancy. Sensors 2021, Vol. 21, Page 4851, 21(14), 4851. https://doi.org/10.3390/S21144851
Donaldo, D., & Benson, B. (1975). A Comparison of Two Approaches to Pure-Inertial and Doppler-Inertial Error Analysis. IEEE Transactions on Aerospace and Electronic Systems, AES-11(4), 447–455. https://doi.org/10.1109/TAES.1975.308106
El-Sheimy, N. (2006). Inertial Techniques and INS/DGPS Integration, ENGO 623-Course Notes . In Department of Geomatics Engineering, University of Calgary, Canada . https://www.scirp.org/reference/referencespapers?referenceid=629302
GNSS OEM. (2023, April 9). RTK, PPP and autonomous . https://gnss.store/blog/post/rtk-ppp-and-autonomous.html?srsltid=AfmBOooqlMkurq5dRqQS0wZSZzkv6nuW4BHoD0PIpF_yJIvyTbkonlO6
Hou, Haiying. (2004). Modeling inertial sensors errors using Allan variance. https://doi.org/10.11575/PRISM/21729
Jafari, M. (2015). Optimal redundant sensor configuration for accuracy increasing in space inertial navigation system. Aerospace Science and Technology, 47, 467–472. https://doi.org/10.1016/J.AST.2015.09.017
John W. Betz. (2015). Engineering Satellite‐Based Navigation and Timing. Engineering Satellite‐Based Navigation and Timing. https://doi.org/10.1002/9781119141167
Joubert, N., Reid, T. G. R., & Noble, F. (2020). Developments in Modern GNSS and Its Impact on Autonomous Vehicle Architectures. IEEE Intelligent Vehicles Symposium, Proceedings, 2029–2036. https://doi.org/10.1109/IV47402.2020.9304840
Konrad, T., & Abel, D. (2022). Combining the Benefits of Tightly and Loosely Coupled GNSS/INS Integration in UAV Applications: A Two-Stage Filter Approach. Proceedings of the International Technical Meeting of The Institute of Navigation, ITM, 2022-January, 735–741. https://doi.org/10.33012/2022.18172
Lehmann, R., & Voß-Böhme, A. (2017). On the statistical power of Baarda’s outlier test and some alternative. Journal of Geodetic Science, 7(1), 68–78. https://doi.org/10.1515/JOGS-2017-0008/MACHINEREADABLECITATION/RIS
Mohammed, Z., Elfadel, I. (Abe) M., & Rasras, M. (2018). Monolithic Multi Degree of Freedom (MDoF) Capacitive MEMS Accelerometers. Micromachines 2018, Vol. 9, Page 602, 9(11), 602. https://doi.org/10.3390/MI9110602
Noureldin, A., Karamat, T. B., & Georgy, J. (2013). Fundamentals of inertial navigation, satellite-based positioning and their integration. Fundamentals of Inertial Navigation, Satellite-Based Positioning and Their Integration, 1–313. https://doi.org/10.1007/978-3-642-30466-8/COVER
Patel, U. N., & Faruque, I. A. (2022). Multi-IMU Based Alternate Navigation Frameworks: Performance Comparison for UAS. IEEE Access, 10, 17565–17577. https://doi.org/10.1109/ACCESS.2022.3144687
Petovello, M. G. (2003). Real-time integration of a tactical-grade IMU and GPS for high-accuracy positioning and navigation. https://doi.org/10.11575/PRISM/23031
Pope, A. J. (1976). The statistics of residuals and the detection of outliers. /view/noaa/30811
S. Godha, G. Lachapelle, & M.E. Cannon. (2006, September). Integrated GPS/INS System for Pedestrian Navigation in a Signal Degraded Environment. https://www.ion.org/publications/abstract.cfm?articleID=7026
SAE International. (2021, May 3). SAE Levels of Driving AutomationTM Refined for Clarity and International Audience. https://www.sae.org/blog/sae-j3016-update
Savage, P. G. (2012a). Strapdown Inertial Navigation Integration Algorithm Design Part 1: Attitude Algorithms. Https://Doi.Org/10.2514/2.4228, 21(1), 19–28. https://doi.org/10.2514/2.4228
Savage, P. G. (2012b). Strapdown Inertial Navigation Integration Algorithm Design Part 2: Velocity and Position Algorithms. Https://Doi.Org/10.2514/2.4242, 21(2), 208–221. https://doi.org/10.2514/2.4242
Scherzinger, B. M. (1996). Inertial navigator error models for large heading uncertainty. Record - IEEE PLANS, Position Location and Navigation Symposium, 477–484. https://doi.org/10.1109/PLANS.1996.509118
Schwarz, K. P., & Wei, M. (2000). INS/GPS Integration for Geodetic Applications: Lecture Notes ENGO 623. Department of Geomatics Eng., The University of Calgary, Calgary, Canada.
Shin, E.-H. (2005). Estimation techniques for low-cost inertial navigation. https://doi.org/10.11575/PRISM/2386
Stephenson, S. (2011). Accuracy Requirements and Benchmarking Position Solutions for Intelligent Transportation Location Based Services. https://www.researchgate.net/publication/260000200_Accuracy_Requirements_and_Benchmarking_Position_Solutions_for_Intelligent_Transportation_Location_Based_Services
Stuart Ferguson. (2019). Precise Positioning Enabled by ST GNSS Receiver and ST IMU. https://www.st.com/content/dam/AME/2019/technology-tour-2019/dallas/presentations/T1S5_Dallas_GNSS_%20IMU_S.Ferguson.pdf
Subirana, J., Zornoza, J. M. J., & Hernández Pajares, M. (2013). GNSS Data Processing Volume I: Fundamentals and Algorithms (ESA TM-23/1, May 2013). I(May), 238. https://gssc.esa.int/navipedia/GNSS_Book/ESA_GNSS-Book_TM-23_Vol_I.pdf
Swaminathan, H. B., Sommer, A., Becker, A., & Atzmueller, M. (2022). Performance Evaluation of GNSS Position Augmentation Methods for Autonomous Vehicles in Urban Environments. Sensors 2022, Vol. 22, Page 8419, 22(21), 8419. https://doi.org/10.3390/S22218419
Trusov, A. A., Prikhodko, I. P., Zotov, S. A., & Shkel, A. M. (2012). High-Q and wide dynamic range inertial MEMS for north-finding and tracking applications. Record - IEEE PLANS, Position Location and Navigation Symposium, 247–251. https://doi.org/10.1109/PLANS.2012.6236888
ullah, F. (farhat), Imad, M. (Muhammad), Hassan, M. A. (Muhammad), Junaid, H. (Hazrat), Faiza, F. (Faiza), & Ahmad, I. (Izaz). (2020). Navigation System for Autonomous Vehicle: A Survey. Journal of Computer Science and Technology Studies, 2(2), 20–35. https://www.neliti.com/publications/589773/
Ünsal, D. (2012). ESTIMATION OF DETERMINISTIC AND STOCHASTIC IMU ERROR PARAMETERS. https://hdl.handle.net/11511/54043
Verma, P., Hajra, K., Banerjee, P., & Bose, A. (2022). Evaluating PDOP in Multi-GNSS Environment. IETE Journal of Research, 68(3), 1705–1712. https://doi.org/10.1080/03772063.2019.1666750
Williams, N., & Barth, M. (2021). A qualitative analysis of vehicle positioning requirements for connected vehicle applications. IEEE Intelligent Transportation Systems Magazine, 13(1), 225–242. https://doi.org/10.1109/MITS.2019.2953521