| 研究生: |
謝旻昆 Sie, Min-Kun |
|---|---|
| 論文名稱: |
基於接收機率之室內定位系統 Indoor Positioning System Based on Received Signal Probability |
| 指導教授: |
郭致宏
Kuo, Chih-Hung |
| 學位類別: |
碩士 Master |
| 系所名稱: |
電機資訊學院 - 電機工程學系 Department of Electrical Engineering |
| 論文出版年: | 2017 |
| 畢業學年度: | 106 |
| 語文別: | 中文 |
| 論文頁數: | 64 |
| 中文關鍵詞: | 藍牙 、藍芽低功率 、室內定位 |
| 外文關鍵詞: | Bluetooth, Bluetooth Low Energy, BLE, Indoor Positioning |
| 相關次數: | 點閱:103 下載:6 |
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近年來因為智慧型手機的發展,室內定位越來越受重視。本論文提出一基於藍牙低功率 (Bluetooth Low Energy, BLE) Beacon 的室內定位方法,充份利用 BLE Beacon 高自訂性,高相容性和便宜的優勢,並利用硬體接收訊號時最低接收訊號強度 (Received Signal Strength, RSS) 的性質定位。先前的定位方法要達到最低的誤差,通常需要特殊硬體,或者單一節點的定位範圍受限,再不然就是計算資源要求較高。有時這些限制會和現代人對智慧型手機的期望相背,例如:有特殊硬體,手機就會變大。運算資源要求高,手機續航力就會減少。和先前的方法比較本論文提出的方法,有較少的的事前準備工作,無需特殊硬體,運算資源要求也不高,同時也達到不錯的定位精度。根據模擬的結果,本論文提出的定位方法,定位誤差可以小於 1.5公尺。
In this paper, we introduce an indoor location estimation method based on the Bluetooth low energy (BLE) beacon. Beacons are advertised at low transmission power level to limit the estimation error. Each beacon point transmits with multiple power levels to increase the utilizing efficiency. We fully take advantage of BLE beacons that are cheap and controllable. Simulation results show that the estimation error of the proposed scheme is less than 1.5 meter.
[1] C. Feng, W. S. A. Au, S. Valaee, and Z. Tan, “Received-signal-strength-based indoor positioning using compressive sensing,” IEEE Transactions on Mobile Computing, vol. 11, pp. 1983–1993, Dec 2012.
[2] J. Xiong and K. Jamieson, “Arraytrack: A fine-grained indoor location system,” in Proceedings of 10th USENIX Symposium on Networked Systems Design and Implementation (NSDI 13), (Lombard, IL), pp. 71–84, USENIX, 2013.
[3] D. Vasisht, S. Kumar, and D. Katabi, “Sub-nanosecond time of flight on commercial wi-fi cards,” in Proceedings of the 2015 ACM Conference on Special Interest Group on Data Communication, SIGCOMM ’15, (New York, NY, USA), pp. 121–122, ACM, 2015.
[4] ITU-R, “Propagation data and prediction methods for the planning of indoor radiocommunication systems and radio local area networks in the frequency range 300 MHz to 100 GHz,” ITU Recommendation P.1238-9, 2017.
[5] T. Singal, Wireless communications. Tata McGraw-Hill Education, 2010.
[6] K. Borre, D. M. Akos, N. Bertelsen, P. Rinder, and S. H. Jensen, A software-defined GPS and Galileo receiver: a single-frequency approach. Springer Science & Business Media, 2007.
[7] A. Dempster, “QZSS’s indoor messaging system, GNSS friend or foe?,” Inside GNSS, vol. 4, no. 1, pp. 37–40, 2009.
[8] L. Pei, R. Chen, J. Liu, T. Tenhunen, H. Kuusniemi, and Y. Chen, “Inquiry-based bluetooth indoor positioning via RSSI probability distributions,” in Proceedings of 2010 Second International Conference on Advances in Satellite and Space Communications (SPACOMM),, pp. 151–156.
[9] Y. Itagaki, A. Suzuki, and T. Iyota, “Indoor positioning for moving objects using a hardware device with spread spectrum ultrasonic waves,” in Proceedings of 2012 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–6.
[10] Bluetooth SIG, “Bluetooth core specification version 4.0,” Specification of the Bluetooth System, 2010.
[11] C. Brignone, T. Connors, G. Lyon, and S. Pradhan, “SmartLOCUS: An autonomous, self-assembling sensor network for indoor asset and systems management,” Mobile Media Syst. Lab., HP Laboratories, Palo Alto, CA, Tech. Rep, 2003.
[12] S. Viswanathan and S. Srinivasan, “Improved path loss prediction model for short range indoor positioning using bluetooth low energy,” in Proceedings of 2015 IEEE SENSORS, pp. 1–4, Nov 2015.
[13] K. Townsend, C. Cufí, R. Davidson, et al., Getting started with Bluetooth low energy: tools and techniques for low-power networking. O’Reilly Media, Inc., 2014.
[14] J. Wilson, N. Patwari, and O. G. Vasquez, “Regularization methods for radio tomographic imaging,” in Proceeding of 2009 Virginia Tech Symposium on Wireless Personal Communications, 2009.
[15] P. Bahl and V. N. Padmanabhan, “RADAR: an in-building rf-based user location and tracking system,” in Proceedings of the IEEE INFOCOM 2000 on 19th Annual Joint Conference of the IEEE Computer and Communications Societies, vol. 2, pp. 775–784 vol.2, 2000.
[16] N. Patwari, J. N. Ash, S. Kyperountas, A. O. Hero, R. L. Moses, and N. S. Correal, “Locating the nodes: cooperative localization in wireless sensor networks,” IEEE Signal Processing Magazine, vol. 22, pp. 54–69, July 2005.
[17] Y. Gwon and R. Jain, “Error characteristics and calibration-free techniques for wireless lan-based location estimation,” in Proceedings of the Second International Workshop on Mobility Management & Wireless Access Protocols, MobiWac ’04, (New York, NY, USA), pp. 2–9, ACM, 2004.
[18] Z. Weissman, “Indoor location,” White paper, Tadlys Ltd http://www.tadlys.co. il/media/downloads/Indoors_Location_Systems.pdf, 2004.
[19] J. Liu, “An IR-UWB indoor positioning based on TOA and band-gap modulations,” in Proceeding of 2017 International Applied Computational Electromagnetics Society Symposium (ACES), pp. 1–2, Aug 2017.
[20] A. E. Cano-García, Y. P. Chacón, and J. L. L. Galilea, “Preliminary simulation for an optical-indoor positioning system based on cyclic-time difference of arrival,” in Proceeding of 2016 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1–8, Oct 2016.
[21] M. Khalaf-Allah, “Differential ultra-wideband (DUWB) for accurate indoor position estimation: Basic concept and simulation results,” in Proceeding of 2013 Saudi International Electronics, Communications and Photonics Conference, pp. 1–4, April 2013.
[22] E. Weisstein, CRC Concise Encyclopedia of Mathematics, Second Edition. CRC Press, 2002.
[23] T. Hastie, R. Tibshirani, and J. Friedman, The Elements of Statistical Learning. Springer Series in Statistics, Springer New York Inc., 2001.