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    题名: 利用粒子濾波器結合室內磁場地圖輔助行人航位推算於室內定位之研究
    The study of using Particle Filter method combined Indoor Magnetic Map to support Pedestrian Dead Reckoning for Indoor Positioning.
    作者: 陳宥竣
    Chen, Yu-Chun
    贡献者: 甯方璽
    Ning, Fang-Shii
    陳宥竣
    Chen, Yu-Chun
    关键词: 室內定位
    行人航位推算
    室內磁場地圖
    粒子濾波器
    Indoor positioning
    Magnetic field map
    Pedestrian Dead Reckoning
    Particle filter
    日期: 2019
    上传时间: 2019-09-05 17:00:29 (UTC+8)
    摘要: 在過去的社會裡，每當我們來到陌生的環境，常會需要一張地圖來指引我們方向。而在科技日新月異的時代裡，隨著全球導航衛星系統(Global Navigation Satellite System, GNSS)的出現，戶外的定位與導航功能已經趨近完善，然而室內定位部分因為訊號受到遮蔽，導致無法接收訊號進行導航定位，也因此室內定位的方式一直是近年來研究和發展的重點。
    翻開室內定位的歷史，過去多為架設感應器來探測使用者的位置，如紅外線定位系統，而近代則多為主動發出訊號的設施，如Wi-Fi、iBeacon、RFID(Radio Frequency IDentification)等，又或者是利用影像、慣性感測元件，甚至是較少被提及的磁場定位技術。上述每種定位技術都有其優缺點，而成本會直接影響室內定位方法的使用門檻，因此本研究選擇利用行動裝置獲取陀螺儀和加速度儀的資訊，用以偵測與推算使用者位置。由於行人航位推算技術會隨時間增加而快速累積誤差，因此本研究加入粒子濾波器的概念，結合室內磁場資訊給予粒子適當權重，以解決行人航位推算快速累積誤差的問題，並達成在合理誤差範圍內完成室內定位之目的。
    本研究除了引入粒子濾波器的概念，也改變了初步估計使用者步長的方式，並透過實驗證明本研究提出之粒子濾波器方法的可行性，且研究結果顯示其定位精度可達到0.6 ~ 0.8 m之水準。
    In the past, whenever we came to an unfamiliar environment, we often needed a map to guide us. With the appearance of Global Navigation Satellite System (GNSS), the outdoor positioning has approached perfection. However, due to the environment obstruction, the indoor signal cannot be received for positioning. Therefore, indoor positioning technology has become the focus of research and development in recent years.
    In the history of indoor positioning, it mostly set up sensors to detect the position of the users, such as infrared positioning system. In recent years, most of the technologies send out signals actively, such as Wi-Fi, iBeacon, RFID, or using images, INS, and even less mentioned Magnetic field positioning technology. All the technologies above have their own advantages and disadvantages, and the cost directly affects the threshold of use of indoor positioning methods. Therefore, this study chose to use the mobile device to obtain information from the gyroscope and accelerometer to detect the path and estimate the user`s position. Because the Pedestrian Dead Reckoning (PDR) technology will accumulate errors quickly with time, this study adds the concept of particle filter, combined with the indoor magnetic information to give particles appropriate weights to solve the problem, and achieve the purpose of indoor positioning within a reasonable margin of error.
    In addition, this study also changed the way estimating the user`s step length, and proved the feasibility of the method proposed in this study. The research results show that the positioning accuracy can reach the level of 0.6 ~ 0.8 meters.
    參考文獻: 中文參考文獻
    曲衍旭, 郭倫嘉, 張聖安, 薛毓弘, 馮堃齊, & 黃義雄. (2012). 一結合無線訊號強度與慣性元件進行跨裝置間定位的系統與方法. 電腦與通訊, (143), 43-48.
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    英文參考文獻
    Attia, M., Moussa, A., & El-Sheimy, N. (2013). Map aided pedestrian dead reckoning using buildings information for indoor navigation applications. Positioning, 4(03), 227.
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    Chung, J., Donahoe, M., Schmandt, C., Kim, I. J., Razavai, P., & Wiseman, M. (2011, June). Indoor location sensing using geo-magnetism. In Proceedings of the 9th international conference on Mobile systems, applications, and services (pp. 141-154). ACM.
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    Groves, P. D. (2013). Principles of GNSS, inertial, and multisensor integrated navigation systems. Artech house.
    Haverinen, J., & Kemppainen, A. (2009). Global indoor self-localization based on the ambient magnetic field. Robotics and Autonomous Systems, 57(10), 1028-1035.
    Huang, H., Qiu, K., Li, W., & Luo, D. (2018). PDR Combined with Magnetic Fingerprint Algorithm for Indoor Positioning. In Multidisciplinary Digital Publishing Institute Proceedings (Vol. 4, No. 1, p. 24).
    Jimenez, A. R., Seco, F., Prieto, C., & Guevara, J. (2009, August). A comparison of pedestrian dead-reckoning algorithms using a low-cost MEMS IMU. In Intelligent Signal Processing, 2009. WISP 2009. IEEE International Symposium on (pp. 37-42). IEEE.
    Le Grand, E., & Thrun, S. (2012, September). 3-axis magnetic field mapping and fusion for indoor localization. In Multisensor Fusion and Integration for Intelligent Systems (MFI), 2012 IEEE Conference on (pp. 358-364). IEEE.
    Lee, N., Ahn, S., & Han, D. (2018). AMID: Accurate Magnetic Indoor Localization Using Deep Learning. Sensors, 18(5), 1598.
    Li, B., Gallagher, T., Dempster, A. G., & Rizos, C. (2012a, November). How feasible is the use of magnetic field alone for indoor positioning?. In Indoor positioning and indoor navigation (ipin), 2012 international conference on (pp. 1-9). IEEE.
    Li, F., Zhao, C., Ding, G., Gong, J., Liu, C., & Zhao, F. (2012b, September). A reliable and accurate indoor localization method using phone inertial sensors. In Proceedings of the 2012 ACM Conference on Ubiquitous Computing (pp. 421-430). ACM.
    Liu, Y., Chen, Y., Shi, L., Tian, Z., Zhou, M., & Li, L. (2015). Accelerometer based joint step detection and adaptive step length estimation algorithm using handheld devices. Journal of Communications, 10(7), 520-525.
    Marschollek, M., Goevercin, M., Wolf, K. H., Song, B., Gietzelt, M., Haux, R., & Steinhagen-Thiessen, E. (2008, August). A performance comparison of accelerometry-based step detection algorithms on a large, non-laboratory sample of healthy and mobility-impaired persons. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE (pp. 1319-1322). IEEE.
    Mautz, R. (2012). Indoor positioning technologies. Doctoral and Habilitation Thesis, ETH Zurich, Department of Civil, Environmental and Geomatic Engineering, Institute of Geodesy and Photogrammetry, Switzerland.
    Pratama, A. R., & Hidayat, R. (2012, September). Smartphone-based pedestrian dead reckoning as an indoor positioning system. In System Engineering and Technology (ICSET), 2012 International Conference on (pp. 1-6). IEEE.
    Renaudin, V., Susi, M., & Lachapelle, G. (2012). Step length estimation using handheld inertial sensors. Sensors, 12(7), 8507-8525.
    Sen, S., Radunovic, B., Choudhury, R. R., & Minka, T. (2012, June). You are facing the Mona Lisa: spot localization using PHY layer information. In Proceedings of the 10th international conference on Mobile systems, applications, and services (pp. 183-196). ACM.
    Shin, S. H., Park, C. G., Kim, J. W., Hong, H. S., & Lee, J. M. (2007, February). Adaptive step length estimation algorithm using low-cost MEMS inertial sensors. In Sensors Applications Symposium, 2007. SAS`07. IEEE (pp. 1-5). IEEE.
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    Xie, H., Gu, T., Tao, X., Ye, H., & Lu, J. (2016). A reliability-augmented particle filter for magnetic fingerprinting based indoor localization on smartphone. IEEE Transactions on Mobile Computing, 15(8), 1877-1892.
    Yang, X., & Huang, B. (2015, May). An accurate step detection algorithm using unconstrained smartphones. In Control and Decision Conference (CCDC), 2015 27th Chinese (pp. 5682-5687). IEEE.
     
    網頁參考文獻
    Android developer, SensorManager, Web: https://developer.android.com/
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    維基百科，HTC One A9 Retrieved August 20, 2018 from 維基百科，自由的百科全書 Web: https://zh.wikipedia.org/wiki/HTC_One_A9
    比價王，Samsung Galaxy S8 Retrieved December 24, 2018 from 億普媒體股份有限公司 Web: https://www.eprice.com.tw/mobile/intro/c01-p5668-samsung-galaxy-s8/
    比價王，SONY Xperia X Performance Retrieved December 22, 2017 from 億普媒體股份有限公司 Web: https://www.eprice.com.tw/mobile/intro/c01-p5446-sony-xperia-x-performance/
    3S Market「全球智慧科技應用」市場資訊網，物聯網【定位技術】超級完全大解析！ Retrieved June 6, 2018 from 3S Market「全球智慧科技應用」市場資訊網 Web: https://3smarket-info.blogspot.com/2018/06/blog-post_39.html
    描述: 碩士
    國立政治大學
    地政學系
    106257031
    資料來源: http://thesis.lib.nccu.edu.tw/record/#G0106257031
    数据类型: thesis
    DOI: 10.6814/NCCU201900672
    显示于类别:[地政學系] 學位論文

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