進階搜尋


   電子論文尚未授權公開,紙本請查館藏目錄
(※如查詢不到或館藏狀況顯示「閉架不公開」,表示該本論文不在書庫,無法取用。)
系統識別號 U0026-3008201914493300
論文名稱(中文) 基於智能手機和iBeacon的融合方法對室內定位
論文名稱(英文) A fusion approach based on smartphone and iBeacon for indoor localization
校院名稱 成功大學
系所名稱(中) 資訊工程學系
系所名稱(英) Institute of Computer Science and Information Engineering
學年度 107
學期 2
出版年 108
研究生(中文) 謝明華
研究生(英文) Ming-Hua Hsieh
學號 P76041205
學位類別 碩士
語文別 英文
論文頁數 81頁
口試委員 指導教授-藍崑展
口試委員-蘇淑茵
口試委員-黃崇明
口試委員-胡敏君
口試委員-蘇柏仁
中文關鍵字 室內定位  深度學習  iBeacon  接受信號強度指示器 
英文關鍵字 indoor positioning  deep learning  iBeacon  Received Signal Strength Indicator 
學科別分類
中文摘要 智慧型手機的進步為現今社會帶來了進一步的應用,這些技術可以提供了大量使用者的信息,也因此諸多具有挑戰性議題也逐漸在開發,其中的一項便為室內定位。
雖然目前已經有不少使用智慧型手機作為基準開發的室內定位技術,但這些技術仍舊在精確度以及一致性有著缺點。近年來iBeacon的普遍性逐漸提升,對於眾多智慧型手機使用著也提供了各項便利的服務,在室內定位當中iBeacon也成為了一個新的選擇,相較於傳統定系統選擇使用的Wi-Fi AP,iBeacon有著低功耗、體積小以及最主要的成本低的優點,由於成本低廉,在一定的室內中間可布置的iBeacon數量就能夠增多,使得訊號接收強度指示系統下可獲得的訊號增多進以提升所需特徵的強度。在我們的研究中,採用的是深度學習的方式用來判別使用者所在位置,用於室內定位的接受信號強度指示器系統已經開發了很長時間。 迄今為止,沒有足夠的特徵提取方法可以顯著減輕接受信號強度指示器變化的影響,這降低了基於DL的室內指紋識別算法的性能。因此,我們從智慧型手機提取慣性傳感器的資料,計算出使用走行走的路徑長度以及方向,便能判斷使用者每一步完後所在的位置,並將其轉換成特徵iBeacon所收到的訊號接收強度指示作結合。我們的目標是通過使用iBeacon和慣性傳感器在室內定位設計取得更高的定位精確度,在真實環境中的經驗實驗表明,在我們的室內定位方案中使用深度學習結合兩種特徵值可以在定位精度,以及資料取得便利性及成本上實現令人滿意的性能。
英文摘要 The rapid advance of smart phone technology has brought further applications to today's society. Those technologies can provide a huge amount of users' information; therefore many challenging issues are gradually emerging, one of which is indoor positioning.
Although there are many indoor positioning technologies developed by utilizing smart phones as the benchmark, those technologies still have a lot of shortages like lacking accuracy and consistency. In recent years, the popularity of iBeacon has gradually be raised, and it has offered a variety of convenient services to many smart phones. In indoor positioning, compared to the Wi-Fi in the traditional system, iBeacon has become a new choice as well. AP, iBeacon has the advantages of low power consumption, small size, and low cost. As far as the low cost is concerned, the number of iBeacons that can be arranged in a specific indoor room can also be increased, so that the signals available under the signal receiving intensity indication system are enhanced to improve the strength of a desired feature. In this study, there is a deep learning approach applied to determine the location of the user. Also, indoor positioning’s received signal strength indicator system has been developed for a long period of time. Therefore, we have tried elicited the data of the inertial sensor by the smart phone, and calculated the length and the walking direction of the path, so that we can determine the position of the user after he/she ends each step in walking. We then have extracted the features of the received RSSI by the featured iBeacon. Then, two kinds of features have been combined for deep learning. The purpose of doing so is to obtain even higher positioning accuracy by utilizing iBeacon and the inertia sensor in indoor positioning design.
論文目次 摘要…………………………………………………………………………...…………iii
Abstract………………………………………………………………….…...………… iv
致謝………………………………………………………………….……..………… vi
Contents………………………………………………………………….…….………… vii
List of Table…………………………………………………………….……..………..… ix
List of Figure………………………………………………………….……..…….……… x
Chapter 1. Introduction………………………………………………………...………… 1
Chapter 2. Related Work……………………………………………………...………….. 3
2.1 Prior work in indoor localization……………………………………..…………3
2.1.1 Optical/vision position system……………………………………….…… 3
2.1.2 Pedestrian Dead Reckoning(PDR)…...………….………………….…….4
2.1.3 Radio signal……………………..…………………………….……………5
2.1.3.1 Angle of Arrival .…………………..………………………….…….5
2.1.3.2 Time of Arrival…………………………………………………6
2.1.3.3 Time Difference of Arrival………………………………………6
2.1.3.4 Received Signal Strength Indication (RSSI)……………………….. 7
2.2 Algorithm based on RSSI………………………………………..…..…………… 7
2.2.1 Trilateration..……….……………..…………………………..……… 8
2.2.2 Triangulation………………..………………………………………. 8
2.2.3 Proximity…………………..………………………………………… 8
2.2.4 Scene Analysis/Fingerprint……….………………………..………… 8
2.3 Indoor position using BLE.……………………………………………..……… 9
2.4 RSSI using machine learning…………………………………………………… 12
2.5 Comparison……………………………………………………………………… 13
Chapter 3. Method…………………………………………………………….………… 14
3.1 Data Collection…………..……………..…………………………………….. 15
3.2 PDR…………………………………………………………………………. 15
3.2.2 Simple Harmonic Motion(SHM)…………………………………….17
3.2.2 Step Recognition Module…………………………………………. 18
3.2.3 Step Length Estimator…………………………………………….. 19
3.3 Particle generation ….…………………………………………………………. 23
3.4 Neural Networks…………………………………………………………. 26
3.4.1 Fully Connected Neural Network………………………………… 27
3.5 Input Data…………………………………………………………………. 28
3.6 Output Data……………………………………………………………………. 29
3.7 Deep learning model parameters setup………………………………………… 29
Chapter 4. Experiment Result & Parameter Compare………………………………. 30
4.1 Experimental description………………………………………………………....31
4.1.1 How to collect data………………………………………………… 31
4.1.2 How to use Deep Learning………………………………………… 36
4.2 localization accuracy…………………………………………………………… 37
4.2.1 Deep Learning using RSS……..……………………………………. 38
4.2.2 Accuracy Using PDR………….……………………………………. 38
4.2.3 Combine two kinds of data……….…………………………………. 39
4.2.4 Compare with prior work…………………………………………… 39
4.3 Effect of different parameters ………………………………………………….. 40
4.3.1 Different Hidden layers…………………...………………………… 40
4.3.2 Different Activation Function ………….……………………………41
4.3.3 Different Optimizer………………….……………………………… 41
4.3.4 Different path……………………………………………………….. 41
4.3.5 Training data increasing and compassion of different smartphones ….41
Chapter 5. Conclusion & Future Work………..……..………………….…………. 44
Reference……………………………………………………………………………… 45
Appendix…………………………………………………………………………………. 50

參考文獻 [1] M. Ciurana, F. Barcelo, and S. Cugno, ”Indoor tracking in WLAN location with TOA measurements,” Proc. ACM International Workshop on Mobility Management and Wireless Access, pp. 121-125, 2006
[2] D. Young, C. Keller, D. Bliss, and K. Forsythe, ‘‘Ultra-wideband (UWB) transmitter location using time difference of arrival (TDOA) techniques,’’ in Proc. Conf. Signals, Syst. Comput., 2003, vol. 2, no. 2, pp. 1225–1229
[3] H. Aoki, B. Schiele, and A. Pentland, “Realtime personal positioning system for a wearable computer”, The Third International Symposium on Wearable Computers, pp. 37-43 (1999)
[4] R. Boris, K. Effrosyni, and D. Marcin, “Mobile museum guide based on fast SIFT recognition”, 6th International Workshop on Adaptive Multimedia Retrieval, pp. 26-27 (2008)
[5] D. Hsu, “Time series forecasting based on augmented Long Short-Term Memory”, arXiv preprint arXiv:1707.00666, (2017)
[6] Mautz, R. & Tilch, S. (2011). Survey of optical indoor positioning systems. In 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–7).
[7] Klopschitz, M., Schall, G., Schmalstieg, D., & Reitmayr, G. (2010). Visual tracking for augmented reality. In 2010 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–4).
[8] Mautz, R. (2012). Indoor positioning technologies (Habilitation thesis, ETH Zurich).
[9] Grewal, M. S., Weill, L. R., & Andrews, A. P. (2007). Global positioning systems, inertial navigation, and integration (2nd). Hoboken: John Wiley & Sons.
[10] Harle, R. (2013). A survey of indoor inertial positioning systems for pedestrians. IEEE Communications Surveys & Tutorials, 15(3), 1281–1293.
[11] Brás, L., Carvalho, N. B., Pinho, P., Kulas, L., & Nyka, K. (2012). A review of antennas for indoor positioning systems. International Journal of Antennas and Propagation, 2012(Article ID 953269), 1–14.
[12] Sharp, I. & Yu, K. (2014). Sensor-based dead-reckoning for indoor positioning. Physical Communication, 13(A), 4–16.
[13] Godha, S. & Cannon, M. E. (2007). GPS/MEMS INS integrated system for navigation in urban areas. GPS Solutions, 11(3), 193–203.
[14] Bird, J. & Arden, D. (2011). Indoor navigation with foot-mounted strapdown inertial navigation and magnetic sensors. IEEE Wireless Communications, 18(2), 28–35.
[15] Weston, J. & Titterton, D. (2000). Modern inertial navigation technology and its application. Electronics and Communication Engineering Journal, 12(2), 49–64.
[16] Randell, C. [C], Djiallis, C., & Müller, H. (2003). Personal position measurement using dead reckoning. In Seventh IEEE International Symposium on Wearable Computers (ISWC ’03) (pp. 166–173).
[17] Goyal, P., Ribeiro, V. J., Saran, H., & Kumar, A. (2011). Strap-down pedestrian dead-reckoning system. In 2011 International Conference on Indoor Positioning and Indoor Navigation (IPIN) (pp. 1–7).
[18] C. Lim, B. P. Ng, and D. Da, ”Robust methods for AOA geo-location in a real-time indoor WiFi system,” Journal of Location Based Services, vol. 2, pp. 112-121, 2008.
[19] Dakkak, M., Nakib, A., Daachi, B., Siarry, P., & Lemoine, J. (2011). Indoor localization method based on RTT and AOA using coordinates clustering. Computer Networks, 55(8), 1794–1803.
[20] Amundson, I. & Koutsoukos, X. D. (2009). A survey on localization for mobile wireless sensor networks. In R. Fuller & X. D. Koutsoukos (Eds.), Mobile entity localization and tracking in gpsless environments (pp. 235–254).
[21] Liu, H., Darabi, H., Banerjee, P., & Liu, J. (2007). Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 37(6), 1067–1080.
[22] M. Ciurana, F. Barcelo, and S. Cugno, ”Indoor tracking in WLAN location with TOA measurements,” Proc. ACM International Workshop on Mobility Management and Wireless Access, pp. 121-125, 2006
[23] Li J, Han G, Zhu C, Sun G (2016a) An indoor ultrasonic positioning system based on TOA for internet of things. Mob Inf Syst.
[24] Nuaimi, K. A. & Kamel, H. (2011). A survey of indoor positioning systems and algorithms. In International Conference on Innovations in Information Technology (pp. 185–190). Abu Dhabi: IEEE.
[25] Hazas, M. & Hopper, A. (2006). Broadband ultrasonic location systems for improved indoor positioning. IEEE Transactions on Mobile Computing, 5(5), 536–547.
[26] Xiao, J., Liu, Z., Yang, Y., Liu, D., & Han, X. (2011). Comparison and analysis of indoor wireless positioning techniques. In International Conference on Computer Science and Service System (pp. 293–296).
[27] Zhang, D. [Da], Xia, F., Yang, Z., Yao, L., & Zhao, W. (2010). Localization technologies for indoor human tracking. In 5th International Conference on Future Information Technology (FutureTech) (p. 6).
[28] Santosh Subedi ; Goo-Rak Kwon ; Seokjoo Shin ; Suk-seung Hwang ; Jae-Young Pyun (2016) Beacon based indoor positioning system using weighted centroid localization approach.
[29] P. Dickinson, G. Cielniak, O. Szymanezyk, and M. Mannion, “Indoor positioning of shoppers using a network of Bluetooth low energy beacons,” in Proc. Int. Conf. Indoor Position. Indoor Navig. (IPIN), Alcalá de Henares, Spain, 2016, pp. 1–8
[30] Teran, M.; Aranda, J.; Carrillo, H.; Mendez, D.; Parra, C. IoT-based System for Indoor Location using Bluetooth Low Energy. In Proceedings of the IEEE Colombian Conference on Communications and Computing (COLCOM2017), Cartagena, Colombia, 16–18 August 2017; IEEE Xplore Digital Library: Cartagena, Colombia, 2017.
[31] M. Mohammadi et al., “Semi-Supervised Deep Reinforcement Learning in Support of IoT and Smart City Services,” IEEE Internet of Things J., 2017, pp. 1--12.
[32] Xiao, C.; Yang, D.; Chen, Z.; Tan, G. 3-D BLE Indoor Localization Based on Denoising Autoencoder. IEEE Access 2017, 5, 12751–12760.
[33] Kamuran Doğuş Yüksel ; Behçet Uğur Töreyin, A deep learning and RSSI based approach for indoor positioning. IEEE Access 2018,5, 17914392
[34] A. Adege, H.-P. Lin, G. Tarekegn, and S.-S. Jeng, “Applying deep neural network (DNN) for robust indoor localization in multi-building environment,” Appl. Sci., vol. 8, no. 7, p. 1062, 2018.
[35] Lan, K.-C.; Shih, W.-Y. Using Smart-Phones and Floor Plans for Indoor Location Tracking-Withdrawn. IEEE Trans. Hum.-Mach. Syst. 2014, 44, 211–221.
[36] D. Zaim and M. Bellafkih, “Bluetooth low energy (BLE) based geomarketing system,” in Proc. 11th Int. Conf. Intell. Syst. Theories Appl. (SITA), Mohammedia, Morocco, Oct. 2016, pp. 1–6.
[37] S. Alletto et al., “An indoor location-aware system for an IoT-based smart museum,” IEEE Internet Things J., vol. 3, no. 2, pp. 244–253, Apr. 2016.
[38] M. S. Gast, Building Applications With iBeacon: Proximity and Location Services With Bluetooth Low Energy. Sebastopol, CA, USA: O’Reilly Media, 2014.
[39] S. A. Cheraghi, V. Namboodiri, and L. Walker, “Guidebeacon: Beaconbased indoor wayfinding for the blind, visually impaired, and disoriented,” in Proc. IEEE Int. Conf. Pervasive Comput. Commun. (PerCom), Kailua, HI, USA, 2017, pp. 121–130.
[40] Mdsupport.org. (2018). MD Support—LowViz Guide. Accessed: Jan. 15, 2018. [Online]. Available: http://www.mdsupport.org/audioguide/
[41] N. Woodward, T. Zonfrelli, A. J. Ruffa, and A. Stevens, “Assessing iBeacons as an assistive tool for blind people in Denmark,” M.S. thesis, Worcester Polytech. Inst., Worcester, MA, USA, 2015.
[42] H. J. Tay, J. Tan, and P. Narasimhan, “A survey of security vulnerabilities in Bluetooth low energy beacons,” Parallel Data Lab., Carnegie Mellon Univ., Pittsburgh, PA, USA, Rep. CMU-PDL-16-109, 2016.
[43] M. Choi, W.-K. Park, and I. Lee, “Smart office energy management system using Bluetooth low energy based beacons and a mobile app,” in Proc. IEEE Int. Conf. Consum Electron. (ICCE), Las Vegas, NV, USA, 2015, pp. 501–502.
[44] A. Akinsiku and D. Jadav, “BeaSmart: A beacon enabled smarter workplace,” in Proc. IEEE/IFIP Netw. Oper. Manage. Symp. (NOMS), Istanbul, Turkey, 2016, pp. 1269–1272.
[45] M. Collotta and G. Pau, “A novel energy management approach for smart homes using Bluetooth low energy,” IEEE J. Sel. Areas Commun., vol. 33, no. 12, pp. 2988–2996, Dec. 2015.
[46] Z. Zhao, J. Fang, G. Q. Huang, and M. Zhang, “iBeacon enabled indoor positioning for warehouse management,” in Proc. 4th Int. Symp. Comput. Bus. Intell. (ISCBI), Olten, Switzerland, 2016, pp. 21–26.
論文全文使用權限
  • 同意授權校內瀏覽/列印電子全文服務,於2024-08-30起公開。
  • 同意授權校外瀏覽/列印電子全文服務,於2024-08-30起公開。


  • 如您有疑問,請聯絡圖書館
    聯絡電話:(06)2757575#65773
    聯絡E-mail:etds@email.ncku.edu.tw