跳到主要內容

臺灣博碩士論文加值系統

(44.210.237.158) 您好!臺灣時間:2022/09/25 22:30
字體大小: 字級放大   字級縮小   預設字形  
回查詢結果 :::

詳目顯示

: 
twitterline
研究生:黃以寧
研究生(外文):Yie-ning Huang
論文名稱(外文):Ultrasound Ranging for Pedestrian Dead Reckoning Systems
指導教授:孫敏德
指導教授(外文):Min-Te Sun
學位類別:碩士
校院名稱:國立中央大學
系所名稱:資訊工程學系
學門:工程學門
學類:電資工程學類
論文種類:學術論文
論文出版年:2014
畢業學年度:102
語文別:英文
論文頁數:35
中文關鍵詞:室內定位超音波測距卡爾漫濾波器
外文關鍵詞:indoor localizationultrasound rangingkalman filterdead reckoning
相關次數:
  • 被引用被引用:0
  • 點閱點閱:180
  • 評分評分:
  • 下載下載:6
  • 收藏至我的研究室書目清單書目收藏:0
近年來,室內定位已成為一個熱門話題。大部分的室內定位的方法需要另外架設基礎設施或額外的訓練來實踐。雖然傳統的行人航位推算系統(PDR)不需要額外的花費及訓練就可以實作在行動裝置上,但它會在短時間內迅速積累誤差,使得結果不被接受。為了解決這個問題,我們提出了基於超音波的合作式行人航位推算系統(UCPDR)。UCPDR的主要思想是結合超音波測距和附近的行人的位置信息,並使用機會式卡爾曼濾波器來改善PDR的準確性。為了評估UCPDR的可行性,我們在iOS平台上實作UCPDR系統,而實驗結果表明,UCPDR能夠透過鄰居的位置信息將誤差限制在4公尺內,也證明UCPDR系統的定位精准度優於PDR系統
Indoor localization has become a popular issue in recent years. Most of the indoor localization approaches either require the availability of an infrastructure or the additional training efforts. While traditional pedestrian dead reckoning (PDR) system can be implemented on mobile devices without additional cost and training, it accumulates errors quickly and leads to unacceptable results after a short period of time. To address this issue, we propose the ultrasound-based collaborative pedestrian dead reckoning system (UCPDR). The main idea of UCPDR is to exploit nearby pedestrians' location information by ultrasound ranging and apply the opportunistic Kalman filter to improve the accuracy of PDR. To evaluate feasibility of UCPDR, a prototype is built on the iOS platform. The conducted experiment results shows that UCPDR is able to limit the localization error within 4m after a long period of time through the help of neighbors' location information. Our UCPDR prototype always achieves a better localization accuracy than the traditional PDR system.
1 Introduction 1
2 Literature Review 4
2.1 Radio Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.2 Ultrasound Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
2.3 Hybrid Techniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
3 Preliminary 7
3.1 Collaborative Pedestrian Dead Reckoning System . . . . . . . . . . . . . . 7
3.2 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4 Ultrasound-Based Pedestrian Dead Reckoning Systems 10
4.1 The Basic Idea . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10
4.2 Proximity Detection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
4.3 Kalman Filter . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14
4.4 Backward Correction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
5 Performance 17
5.1 Experiment of two pedestrians . . . . . . . . . . . . . . . . . . . . . . . . . 18
6 Conclusion 21
Reference 22

[1] Base transceiver station. http://en.wikipedia.org/wiki/Base_transceiver_station.
[2] Matrix laboratory(malab). http://www.mathworks.com/products/matlab/.
[3] P. Bahl and V.N. Padmanabhan. Radar: an in-building rf-based user location and tracking system. In INFOCOM 2000. Nineteenth Annual Joint Conference of the IEEE Computer and Communications Societies. Proceedings. IEEE, volume 2, pages775–784 vol.2, 2000.
[4] R. Bajaj, S.L. Ranaweera, and D.P. Agrawal. Gps: location-tracking technology. Computer, 35(4):92–94, Apr 2002.
[5] Krishna Chintalapudi, Anand Padmanabha Iyer, and Venkata N. Padmanabhan. Indoor localization without the pain. In Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking, MobiCom ’10, pages173–184, New York, NY, USA, 2010. ACM.
[6] Ionut Constandache, Xuan Bao, Martin Azizyan, and Romit Roy Choudhury. Did you see bob?: Human localization using mobile phones. In Proceedings of the Sixteenth Annual International Conference on Mobile Computing and Networking, MobiCom’10, pages 149–160, New York, NY, USA, 2010. ACM.
[7] Silke Feldmann, Kyandoghere Kyamakya, Ana Zapater, and Zighuo Lue. An indoor bluetooth-based positioning system: Concept, implementation and experimental evaluation. In Weihua Zhuang, Chi-Hsiang Yeh, Olaf Droegehorn, C.-T. Toh, and Hamid R. Arabnia, editors, International Conference on Wireless Networks, pages109–113. CSREA Press, 2003.
[8] E. Foxlin. Pedestrian tracking with shoe-mounted inertial sensors. Computer Graphics and Applications, IEEE, 25(6):38–46, Nov 2005.
[9] Andy Harter, Andy Hopper, Pete Steggles, Andy Ward, and Paul Webster. The anatomy of a context-aware application. Wirel. Netw., 8(2/3):187–197, March 2002.
[10] K. Kaemarungsi and P. Krishnamurthy. Modeling of indoor positioning systems based on location fingerprinting. In INFOCOM 2004. Twenty-third AnnualJoint Conference of the IEEE Computer and Communications Societies, volume 2, pages1012–1022 vol.2, March 2004.
[11] Yi-Ting Li, Guaning Chen, and Min-Te Sun. An indoor collaborative pedestrian dead reckoning system. In Parallel Processing (ICPP), 2013 42nd International Conference on, pages 923–930, Oct 2013.
[12] Hongbo Liu, Yu Gan, Jie Yang, Simon Sidhom, Yan Wang, Yingying Chen, and Fan Ye. Push the limit of wifi based localization for smartphones. In Proceedings of the 18th Annual International Conference on Mobile Computing and Networking, Mobicom ’12, pages 305–316, New York, NY, USA, 2012. ACM.
[13] Chunyi Peng, Guobin Shen, Yongguang Zhang, Yanlin Li, and Kun Tan. Beepbeep: A high accuracy acoustic ranging system using cots mobile devices. In Proceedings of the 5th International Conference on Embedded Networked Sensor Systems, SenSys’07, pages 1–14, New York, NY, USA, 2007. ACM.
[14] Nissanka B. Priyantha, Anit Chakraborty, and Hari Balakrishnan. The cricket location-support system. In Proceedings of the 6th Annual International Conference on Mobile Computing and Networking, MobiCom’00, pages 32–43, New York,NY, USA, 2000. ACM.
[15] Cli↵ Randell, C. Djiallis, and H. Muller. Personal position measurement using dead reckoning. In Wearable Computers, 2003. Proceedings. Seventh IEEE International Symposium on, pages 166–173, Oct 2003.
[16] Stephen P. Tarzia, Peter A. Dinda, Robert P. Dick, and Gokhan Memik. Indoor localization without infrastructure using the acoustic background spectrum. In Proceedings of the 9th International Conference on Mobile Systems, Applications, and Services, MobiSys ’11, pages 155–168, New York, NY, USA, 2011. ACM.
[17] Benjamin Thiel, Kamil Kloch, and Paul Lukowicz. Sound-based proximity detection with mobile phones. In Proceedings of the Third International Workshop on Sensing Applications on Mobile Phones, PhoneSense ’12, pages 4:1–4:4, New York, NY, USA, 2012. ACM.
[18] M. Uddin and T. Nadeem. Rf-beep: A light ranging scheme for smart devices. In Pervasive Computing and Communications (PerCom), 2013 IEEE International Conference on, pages 114–122, March 2013.
[19] Greg Welch and Gary Bishop. An introduction to the kalman filter. Technical report, Chapel Hill, NC, USA, 1995.
[20] Moustafa Youssef and Ashok Agrawala. The horus wlan location determination system. In Proceedings of the 3rd International Conference on Mobile Systems, Ap-plications, and Services, MobiSys ’05, pages 205-218, New York, NY, USA, 2005. ACM.
[21] Zengbin Zhang, David Chu, Xiaomeng Chen, and Thomas Moscibroda. Swordfight: Enabling a new class of phone-to-phone action games on commodity phones. In Proceedings of the 10th International Conference on Mobile Systems, Applications, and Services, MobiSys ’12, pages 1–14, New York, NY, USA, 2012. ACM.
連結至畢業學校之論文網頁點我開啟連結
註: 此連結為研究生畢業學校所提供,不一定有電子全文可供下載,若連結有誤,請點選上方之〝勘誤回報〞功能,我們會盡快修正,謝謝!
QRCODE
 
 
 
 
 
                                                                                                                                                                                                                                                                                                                                                                                                               
第一頁 上一頁 下一頁 最後一頁 top