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題名 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貢獻者 甯方璽
Ning, Fang-Shii
陳宥竣
Chen, Yu-Chun關鍵詞 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 上傳時間 5-Sep-2019 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.江凱偉、曾義星、呂學展、張秀雯，2017，「106 年度移動載台測量製圖技術發展工作案期末報告」，內政部地政司。吳東旂. (2016). 利用智慧型行動裝置與場景約制進行室內導航定位之研究 (Doctoral dissertation, 吳東旂).陳會安，2015，『新觀念 Android 程式設計範例教本: 使用 Android Studio (附光碟)』，台北：旗標科技股份有限公司。彭威然. (2014). 使用手機加速度計和陀螺儀之室內定位. 淡江大學資訊工程學系資訊網路與通訊碩士班學位論文, 1-85. 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.Beauregard, S., & Haas, H. (2006, March). Pedestrian dead reckoning: A basis for personal positioning. In Proceedings of the 3rd Workshop on Positioning, Navigation and Communication (pp. 27-35).Chen, G., Meng, X., Wang, Y., Zhang, Y., Tian, P., & Yang, H. (2015). Integrated WiFi/PDR/Smartphone using an unscented kalman filter algorithm for 3D indoor localization. Sensors, 15(9), 24595-24614.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.Gozick, B., Subbu, K. P., Dantu, R., & Maeshiro, T. (2011). Magnetic maps for indoor navigation. IEEE Transactions on Instrumentation and Measurement, 60(12), 3883-3891.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.Smith, A., & Page, D. (2015). US smartphone use in 2015. Pew Research Center, 1.Sterling, G. (2014). Magnetic positioning? the arrival of indoor gps. report of Opus Research.Storms, W., Shockley, J., & Raquet, J. (2010, October). Magnetic field navigation in an indoor environment. In Ubiquitous Positioning Indoor Navigation and Location Based Service (UPINLBS), 2010 (pp. 1-10). IEEE.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/Gartner 市場研究機構：http://www.eettaiwan.com/SEARCH/ART/gartner.HTM一點資訊，手機中常用感測器以及原理用途 Retrieved April 7, 2017 from Zi 字媒體/3C科技 Web: https://zi.media/@yidianzixun/post/jCDMgP微學苑，Android的系統架構 Retrieved 2011-2015 from 微學苑Web: http://www.weixueyuan.net/view/6352.html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 資料類型 thesis dc.contributor.advisor 甯方璽 zh_TW dc.contributor.advisor Ning, Fang-Shii en_US dc.contributor.author (Authors) 陳宥竣 zh_TW dc.contributor.author (Authors) Chen, Yu-Chun en_US dc.creator (作者) 陳宥竣 zh_TW dc.creator (作者) Chen, Yu-Chun en_US dc.date (日期) 2019 en_US dc.date.accessioned 5-Sep-2019 17:00:29 (UTC+8) - dc.date.available 5-Sep-2019 17:00:29 (UTC+8) - dc.date.issued (上傳時間) 5-Sep-2019 17:00:29 (UTC+8) - dc.identifier (Other Identifiers) G0106257031 en_US dc.identifier.uri (URI) http://nccur.lib.nccu.edu.tw/handle/140.119/125780 - dc.description (描述) 碩士 zh_TW dc.description (描述) 國立政治大學 zh_TW dc.description (描述) 地政學系 zh_TW dc.description (描述) 106257031 zh_TW dc.description.abstract (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。 zh_TW dc.description.abstract (摘要) 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. en_US dc.description.tableofcontents 謝誌 II摘要 IVAbstract VI目錄 VIII圖目錄 XII表目錄 XVI第一章 緒論 1第一節 研究背景與動機 1第二節 研究目的 3第三節 論文架構 4第二章 文獻回顧 5第一節 行人航位推算 6一、 步伐偵測 8二、 方位估計 11三、 步長估計 13第二節 磁場定位技術 16一、 室內磁場特性 16二、 室內環境的磁場干擾 18三、 磁場資料收集 19四、 磁場定位技術 22第三節 粒子濾波器 23一、 改正方位及步長估計之偏誤 24二、 修改利用磁場之方法 25三、 其它方法與結果 26第四節 Android 系統 28一、Android 系統架構 28二、Android活動生命週期 30第三章 研究方法 33第一節 研究範圍與工具 33一、研究範圍與路線 33二、研究工具 33第二節 研究流程 38第三節 研究方法與理論基礎 41一、 訓練階段 41二、 數據前處理 43三、 粒子濾波器 46第四章 實驗成果與討論分析 49第一節 建立室內磁場地圖資料庫 49第二節 步伐偵測與步長估計實驗 53一、 誤差步數實驗 54二、 係數調整實驗 56三、 前期實驗小結 56第三節 粒子濾波器實驗分析 57一、 測試直線與繞行一圈 58二、 不同使用者行走完整路徑 64三、 討論分析與小結 67第四節 不同變數之實驗成果 72一、 不同性別使用者 72二、 不同廠牌行動裝置 77三、 討論分析 81第五章 結論與建議 85第一節 結論 85一、 步伐偵測與步長估計 85二、 前後粒子濾波器比較分析 86三、 不同變數精度分析 87四、 精度比較與結論 87第二節 建議 89參考文獻 91 zh_TW dc.format.extent 5122869 bytes - dc.format.mimetype application/pdf - dc.source.uri (資料來源) http://thesis.lib.nccu.edu.tw/record/#G0106257031 en_US dc.subject (關鍵詞) 室內定位 zh_TW dc.subject (關鍵詞) 行人航位推算 zh_TW dc.subject (關鍵詞) 室內磁場地圖 zh_TW dc.subject (關鍵詞) 粒子濾波器 zh_TW dc.subject (關鍵詞) Indoor positioning en_US dc.subject (關鍵詞) Magnetic field map en_US dc.subject (關鍵詞) Pedestrian Dead Reckoning en_US dc.subject (關鍵詞) Particle filter en_US dc.title (題名) 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 zh_TW dc.title (題名) The study of using Particle Filter method combined Indoor Magnetic Map to support Pedestrian Dead Reckoning for Indoor Positioning. en_US dc.type (資料類型) thesis en_US dc.relation.reference (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.江凱偉、曾義星、呂學展、張秀雯，2017，「106 年度移動載台測量製圖技術發展工作案期末報告」，內政部地政司。吳東旂. (2016). 利用智慧型行動裝置與場景約制進行室內導航定位之研究 (Doctoral dissertation, 吳東旂).陳會安，2015，『新觀念 Android 程式設計範例教本: 使用 Android Studio (附光碟)』，台北：旗標科技股份有限公司。彭威然. (2014). 使用手機加速度計和陀螺儀之室內定位. 淡江大學資訊工程學系資訊網路與通訊碩士班學位論文, 1-85. 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.Beauregard, S., & Haas, H. (2006, March). Pedestrian dead reckoning: A basis for personal positioning. In Proceedings of the 3rd Workshop on Positioning, Navigation and Communication (pp. 27-35).Chen, G., Meng, X., Wang, Y., Zhang, Y., Tian, P., & Yang, H. (2015). Integrated WiFi/PDR/Smartphone using an unscented kalman filter algorithm for 3D indoor localization. Sensors, 15(9), 24595-24614.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.Gozick, B., Subbu, K. P., Dantu, R., & Maeshiro, T. (2011). Magnetic maps for indoor navigation. IEEE Transactions on Instrumentation and Measurement, 60(12), 3883-3891.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). 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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.Smith, A., & Page, D. (2015). US smartphone use in 2015. Pew Research Center, 1.Sterling, G. (2014). Magnetic positioning? the arrival of indoor gps. report of Opus Research.Storms, W., Shockley, J., & Raquet, J. (2010, October). Magnetic field navigation in an indoor environment. 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