近年來，為了因應快速擴展的地面車載導航市場，如何追求更準確、更高效率之導航便成為諸多研究之主軸。在衛星定位系統尚未出現之前，慣性導航系統(Inertial Navigation System, INS)為車載導航的主要方法，然而，其誤差累積的特性，使慣性導航系統不適用於長時間之應用，因此，當衛星導航系統(Global Navigation Satellite System, GNSS)問世後，整合式導航的概念便用以克服單一系統之缺點，以提供長時間穩定且精確的導航解。最常見之整合導航架構為鬆耦合架構(Loosely-coupled Integration)，其架構簡單，易於即時應用之實現，然而，鬆耦合架構受限於透空度，尤其現代都市環境之惡劣，常使整合系統缺乏衛星觀測量；反觀緊耦合整合架構(Tightly-coupled Integration)，其架構於衛星數不足四顆之情況下仍可獲得衛星更新量。然而，由於緊耦合利用衛星原始觀測量作為更新資訊，故觀測量之品質將直接影響卡曼濾波器(Kalman filter)之估計成果，故異常觀測量之偵測與剔除將成為重要議題。
除了整合系統本身之穩健，外部輔助亦常用於提升整體精度，以車載應用而言，輪速計為常見之配備，利用輪速計提供之速度資訊，能協助零速更新(Zero update)及非和諧約制(Non-holonomic constraint)等約制之實現。而垂直方向之約制，常以氣壓計提供之高程資訊進行。
綜觀前述，本研究著重於多種輔助應用於INS/GNSS緊耦合整合系統之成效。首先將從整合式導航開始，涵蓋各系統之基礎與其運算核心，接著為輪速計、氣壓計之輔助模型，GNSS異常觀測量之偵測與剔除亦將詳細介紹。最後實驗部分，將以惡劣環境作為實驗場域以驗證本研究之成效。

The market of land vehicle navigation has grown rapidly for the past decades. Therefore, studies on achieving higher accuracy and efficiency on vehicle guidance are frequently proposed. Before the first appearance of the satellite system, Inertial Navigation System (INS) has dominated the field of navigation for decades. However, the unbounded error accumulation makes it unsuitable for long-term utilization. Thus, after the publication of the Global Navigation Satellite System (GNSS), INS/GNSS integration system has been proposed for obtaining steady and accurate navigation results by compensating the disadvantages of each other system. The most common integration strategy is the Loosely-Coupled (LC) integration. However, the poor sky vision frustrates the LC for blocking satellite signals in modern urban areas. To overcome this setback, the Tightly-Coupled (TC) integration is considered for which can still obtain GNSS measurements with insufficient available satellites. When it comes to tightly-coupled integration, the quality of the GNSS measurements becomes crucial since the measurements directly affect the estimation results of Kalman Filter (KF). Thus, the abnormal GNSS measurements detecting and eliminating decide the robustness of the system.
Despite the robustness of the INS/GNSS integration system, additional aiding schemes are applied for superior accuracy as well. For land vehicle navigation, the odometer is widely used to measure the speed of the vehicle. Such information can be used to achieve multiple constraints such as Zero Update (ZUPT), Zero Integrated Heading Rate (ZIHR), Non-Holonomic Constraint (NHC), etc. As for height direction, the barometer is usually mentioned for its capability of providing external height information.
This study focuses on the performance analysis of various aiding schemes based on INS/GNSS tightly-coupled integration. First, the basic style of INS/GNSS tightly-coupled integration is introduced, including the principles of both systems and Kalman filtering. Following up is the various aiding schemes which conclude the assistance of the odometer and the barometer. The adjustment of satellite selection realized by abnormal GNSS measurement checking is also highlighted. As for the experiment, testing ground with harsh environments is chosen for the verification of the proposed scheme. The results are shown and summarized at the end of the study.

Aggarwal, P., Syed, Z., Niu, X., & El-Sheimy, N. (2008). A standard testing and calibration procedure for low cost MEMS inertial sensors and units. The Journal of Navigation, 61(2), 323-336.
Aggarwal, P. (2010). MEMS-based integrated navigation. Artech House.
Angrisano, A. (2010). GNSS/INS integration methods. Ph.D. thesis, Universita’degli Studi di Napoli Parthenope.
Barbour, N. M. (2010). Inertial navigation sensors. NATO Science and Technology Organization.
Bloch, A. M., Marsden, J. E., & Zenkov, D. V. (2005). Nonholonomic dynamics. Notices of the AMS, 52(3), 320-329.
Borre, K., Akos, D. M., Bertelsen, N., Rinder, P., & Jensen, S. H. (2007). A software-defined GPS and Galileo receiver: a single-frequency approach. Springer Science & Business Media.
Britting, K. R. (1971). Inertial navigation systems analysis. John Wiley & Sons, Inc.
Brown, R. G., & Hwang, P. Y. (1997). Introduction to random signals and applied Kalman filtering: with MATLAB exercises and solutions. John Wiley & Sons, Inc.
Chen, D. (1994). Development of a fast ambiguity search filtering (FASF) method for GPS carrier phase ambiguity resolution. Ph.D. thesis, University of Calgary. UCGE Report 20071.
Chiang, K. W., & Huang, Y. W. (2008). An intelligent navigator for seamless INS/GPS integrated land vehicle navigation applications. Applied Soft Computing, 8(1), 722-733.
Chiang, K. W., Duong, T. T., & Liao, J. K. (2013). The performance analysis of a real-time integrated INS/GPS vehicle navigation system with abnormal GPS measurement elimination. Sensors, 13(8), 10599-10622.
Collins, J. P., & Langley, R. B. (1999). Possible weighting schemes for GPS carrier phase observations in the presence of multipath. Final contract report for the US Army Corps of Engineers Topographic Engineering Center, No. DAAH04-96-C-0086/TCN, 98151.
Dissanayake, G., Sukkarieh, S., Nebot, E., & Durrant-Whyte, H. (2001). The aiding of a low-cost strapdown inertial measurement unit using vehicle model constraints for land vehicle applications. IEEE transactions on robotics and automation, 17(5), 731-747.
Farrell, J., & Barth, M. (1999). The global positioning system and inertial navigation (Vol. 61). New York, USA: Mcgraw-hill.
Finch, T. (2009). Incremental calculation of weighted mean and variance. University of Cambridge.
Godha, S. (2006). Performance evaluation of low cost MEMS-based IMU integrated with GPS for land vehicle navigation application. Master thesis, University of Calgary. UCGE Report 20239.
Godha, S., Lachapelle, G., & Cannon, M. E. (2006). Integrated GPS/INS system for pedestrian navigation in a signal degraded environment. In ION GNSS (Vol. 2006).
Grewal, M. S., Weill, L. R., & Andrews, A. P. (2007). Global positioning systems, inertial navigation, and integration. John Wiley & Sons, Inc.
Groves, P. D. (2013). Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech house.
Hajj, G. A., Kursinski, E. R., Romans, L. J., Bertiger, W. I., & Leroy, S. S. (2002). A technical description of atmospheric sounding by GPS occultation. Journal of Atmospheric and Solar-Terrestrial Physics, 64(4), 451-469.
Hofmann-Wellenhof, B., Lichtenegger, H., & Collins, J. (2012). Global positioning system: theory and practice. Springer Science & Business Media.
Holm, R., Schou, H., Petersen, H. R., & Normann, S. (2017). Sharing historical data on tactical-grade MEMS-based IMUs delivered to global customers for almost a decade. In Inertial Sensors and Systems (ISS), 2017 (pp. 1-16). IEEE.
Hoque, M. M., Jakowski, N., & Berdermann, J. (2017). Ionospheric correction using NTCM driven by GPS Klobuchar coefficients for GNSS applications. GPS Solutions, 21(4), 1563-1572.
Hou, H. (2004). Modeling inertial sensors errors using Allan variance. Master thesis, University of Calgary. UCGE Report 20201.
Hsu, L. T., Jan, S. S., Groves, P. D., & Kubo, N. (2015). Multipath mitigation and NLOS detection using vector tracking in urban environments. GPS Solutions, 19(2), 249-262.
King, A. D. (1998). Inertial navigation-forty years of evolution. GEC review, 13(3), 140-149.
Klobuchar, J. A. (1987). Ionospheric time-delay algorithm for single-frequency GPS users. IEEE Transactions on aerospace and electronic systems, (3), 325-331.
Knuth, D. (1998). The Art of Computer Programming vol. 2 Seminumerical Algorithms. Pearson Education.
Lau, L., & Mok, E. (1999). Improvement of GPS relative positioning accuracy by using SNR. Journal of surveying engineering, 125(4), 185-202.
Liao, J. K., Zhou, Z. M., Tsai, G. J., Duong, T., & Chiang, K. (2014). The applicability analysis of using smart phones for indoor mobile mapping applications. In Proc. of ION GNSS (pp. 510-531).
Misra, P., & Enge, P. (2001). Global positioning system: Signals, measurements and performance. Massachusetts: Ganga-Jamuna Press.
Nilsson, J. O., Skog, I., & Händel, P. (2012). A note on the limitations of ZUPTs and the implications on sensor error modeling. In 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), 13-15th November 2012.
Obst, M., Bauer, S., & Wanielik, G. (2012). Urban multipath detection and mitigation with dynamic 3D maps for reliable land vehicle localization. In Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION (pp. 685-691). IEEE.
Park, M. (2004). Error analysis and stochastic modeling of MEMS based inertial sensors for land vehicle navigation applications. Master thesis, University of Calgary. UCGE Report 20194.
Park, J., Lee, D., & Park, C. (2015). Implementation of vehicle navigation system using GNSS INS odometer and barometer. J. Positioning, Navig., Timing, 4(3), 141-150.
Parviainen, J., Kantola, J., & Collin, J. (2008). Differential barometry in personal navigation. In Position, Location and Navigation Symposium, 2008 IEEE/ION (pp. 148-152). IEEE.
Perlmutter, M., & Robin, L. (2012). High-performance, low cost inertial MEMS: A market in motion!. In Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION (pp. 225-229). IEEE.
Petovello, M. G. (2003). Real-time integration of a tactical-grade IMU and GPS for high-accuracy positioning and navigation. Ph.D. thesis, University of Calgary. UCGE Report 20173.
Petovello, M. G. (2015). How does a GNSS receiver estimate velocity?. Inside GNSS, 38-41.
Roddy, D. (2006). Satellite Communications, (Professional Engineering). McGraw-Hill Professional: New York.
Rogers, R. M. (2003). Applied mathematics in integrated navigation systems (Vol. 1). AIAA.
Saastamoinen, J. (1972). Contributions to the theory of atmospheric refraction. Bulletin Géodésique (1946-1975), 105(1), 279-298.
Satirapod, C., & Chalermwattanachai, P. (2005). Impact of Different Tropospheric Models on GPS Baseline Accuracy: Case Study in Thailand. Journal of Global Positioning Systems, 4(1-2), 36-40.
Schmidt, G. T., & Phillips, R. E. (2010). INS/GPS integration architectures. NATO RTO Lecture Series.
Shin, E. H. (2001). Accuarcy improvement of low cost INS/GPS for land applications. Master thesis, University of Calgary. UCGE Report 20156.
Shin, E. H. (2005). Estimation techniques for low-cost inertial navigation. Ph.D. thesis, University of Calgary. UCGE Report 20219.
Sukkarieh, S. (2000). Low cost, high integrity, aided inertial navigation systems for autonomous land vehicles. Australian Center for Fields Robobtics, University of Sydney.
Syed, Z. F. (2009). Design and implementation issues of a portable navigation system. Ph.D. thesis, University of Calgary. UCGE Report 20288.
Tanigawa, M., Luinge, H., Schipper, L., & Slycke, P. (2008). Drift-free dynamic height sensor using MEMS IMU aided by MEMS pressure sensor. In Positioning, Navigation and Communication, 2008. WPNC 2008. 5th Workshop on (pp. 191-196). IEEE.
Titterton, D. & Weston, J. (2004). Strapdown inertial navigation technology (Vol. 17). IET.
Townsend, B., & Fenton, P. (1994). A practical approach to the reduction of pseudorange multipath errors in a L1 GPS receiver. In Proceedings of the 7th International Technical Meeting of the Satellite Division of the Institute of Navigation, Salt Lake City, UT, USA.
Wei, M., & Schwarz, K. P. (1990). A Strapdown Inertial Algorithm Using an Earth‐Fixed Cartesian Frame. Navigation, 37(2), 153-167.
Whittaker, W., & Nastro, L. (2006). Utilization of position and orientation data for preplanning and real time autonomous vehicle navigation. In Position, Location, And Navigation Symposium, 2006 IEEE/ION (pp. 372-377). IEEE.
Yang, Y. (2008). Tightly coupled MEMS INS/GPS integration with INS aided receiver tracking loops. Ph.D. thesis, University of Calgary. UCGE Report 20270.
Zhang, M., Vydhyanathan, A., Young, A., & Luinge, H. (2012). Robust height tracking by proper accounting of nonlinearities in an integrated UWB/MEMS-based-IMU/baro system. In Position Location and Navigation Symposium (PLANS), 2012 IEEE/ION (pp. 414-421). IEEE.
Zhou, Q., Zhang, H., Li, Y., & Li, Z. (2015). An Adaptive Low-Cost GNSS/MEMS-IMU Tightly-Coupled Integration System with Aiding Measurement in a GNSS Signal-Challenged Environment. Sensors, 15(9), 23953-23982.