跳到主要內容

臺灣博碩士論文加值系統

(44.201.97.0) 您好!臺灣時間:2024/04/19 14:12
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:李泓毅
研究生(外文):Li, Hung Yi
論文名稱:基於無線訊號接收強度之低複雜度低功耗室內定位系統
論文名稱(外文):A Low Complexity Low Power Indoor Positioning System Based on Wireless Received Signal Strength
指導教授:馬席彬
指導教授(外文):Ma, Hsi Pin
學位類別:碩士
校院名稱:國立清華大學
系所名稱:電機工程學系
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:2016
畢業學年度:105
語文別:英文
論文頁數:69
中文關鍵詞:藍牙低功耗低複雜度室內定位接收訊號強度
外文關鍵詞:Bluetoothlow powerBLElow complexityindoor positioningRSSI
相關次數:
  • 被引用被引用:0
  • 點閱點閱:546
  • 評分評分:
  • 下載下載:83
  • 收藏至我的研究室書目清單書目收藏:0
近年來,定位相關的應用越來越普及。由於全球定位系統(Global positioning system,GPS)的衛星訊號不能夠穿透建築物到室內,所以室內定位系統需要用其他的技術來設計。在本篇論文中,我們利用藍牙低功耗(Bluetooth low energy,BLE)設計了一個低功耗、低成本的室內定位系統。
本篇論文提出的定位系統是由藍牙低功耗標籤(BLE tag)、藍牙低功耗/無線網路中繼器(BLE/Wi-Fi repeater)和伺服器(Fusion server)所構成。系統中的藍牙低功耗標籤負責廣播藍牙信標,中繼器則會接收標籤傳出的信標並萃取出接收到的訊號強度(Received signal strength,RSS),萃取出的訊號強度資訊會透過無線網路(Wi-Fi)上傳到伺服器,之後伺服器會利用訊號強度相關的定位演算法來估測標籤的位置。本篇論文使用了接收訊號強度指紋(Received signal strength indication-fingerprint)與單位原點(Cell of origin,CoO)的混合式演算法來估測目標位置,並對個別的演算法做改良來提升精準度。
為了驗證系統的效能,本篇論文實際考量了兩個不同的室內場景,第一個場景位於清華大學台達館8樓的休息區,第二個則是捷螺系統公司的辦公室,每個場景各被4個中繼器涵蓋,平均的定位誤差分別為1.2公尺及1.37公尺。除此之外我們使用的藍牙低功耗標籤大小半徑是1.7公分,厚度是0.5公分,可以非常簡易的黏貼在定位目標上。每個藍牙低功耗標籤的成本是3美元,對於需要大量的標籤來做多目標定位的應用不會造成太大的負擔。耗能的部分,標籤平均的消耗電流是50微安培,搭配上CR2025鋰電池可以連續使用136天。
In recent years, applications of positioning have become more and more popular. Since the signals transmitted from global positioning system (GPS) satellites cannot penetrate inside the buildings, there are demands for indoor positioning systems with other technologies. In this thesis, we propose a indoor positioning system based on Bluetooth low energy (BLE)
with characteristics of low power, low cost and high portability .
The proposed system consists of BLE tags, BLE/WiFi repeaters and a fusion server. The BLE tag in our system is a device which broadcasts BLE beacons. The BLE/WiFi repeaters collect the beacons transmitted from the tag and extract the received signal strength (RSS). The RSS values are then transmitted to our fusion server through Wi-Fi, and the server will estimate the position of the BLE tag with RSS-based positioning algorithm. We propose
a indoor positioning algorithm which is a hybrid from received signal strength indication (RSSI)-fingerprint and cell of origin (CoO). Some modifications are made to typical RSSI-fingerprint and CoO algorithm to get better accuracy.
To verify the performance of our system, we take two indoor environments into considerate. The first is the rest area of Delta building in National Tsing-Hua University (NTHU). The second is the office of GYRO system company. Each environment is covered by four BLE/WiFi repeaters. The mean error distance of these two environments are 1.2 m and 1.37 m respectively. Moreover, the size of the BLE tag is 1.7 cm in radius and 0.5 cm thick, that can be easily attached to the localization target. Each BLE tag costs 3 US dollars. So it is friendly for those who need large amounts of them for multi-objects positioning. The current consumption of the tag is 50 A which can be used without charge for 136 days with a CR2025 battery.
1.Introduction (P1)
2.Overview of Indoor Positioning Technologies (P5)
3.Proposed System and Algorithm (P21)
4.Implementation Results (P43)
5.Conclusions and Future Work (P61)
[1] J. S. Lee, Y. W. Su, and C. C. Shen, “A comparative study of wireless protocols: Bluetooth, UWB, ZigBee, and Wi-Fi,” in Industrial Electronics Society, 2007. IECON 2007. 33rd Annual Conference of the IEEE, Nov 2007, pp. 46–51.

[2] B. Hu, “Wi-Fi based indoor positioning system using smartphones,” Ph.D. dissertation, RMIT University, 2013.

[3] P. C. Chen, “Indoor positioning and tracking system for drones,” Master’s thesis, National Tsing Hua University, 2015.

[4] 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, vol. 2, 2000, pp. 775–784 vol.2.

[5] G. Welch and G. Bishop, “An introduction to the Kalman filter,” Proceedings of the Siggraph Course, Los Angeles, 2001.

[6] M. A. Al-Ammar, S. Alhadhrami, A. Al-Salman, A. Alarifi, H. S. Al-Khalifa, A. Alnafessah, and M. Alsaleh, “Comparative survey of indoor positioning technologies, techniques, and algorithms,” in Cyberworlds (CW), 2014 International Conference on, Oct 2014, pp. 245–252.

[7] R. Mautz, “Indoor positioning technologies,” Habilitation Thesis, ETH Zurich, February 2012.

[8] H. Liu, H. Darabi, P. Banerjee, and J. Liu, “Survey of wireless indoor positioning techniques and systems,” IEEE Transactions on Systems, Man, and Cybernetics, Part C (Applications and Reviews), vol. 37, no. 6, pp. 1067–1080, Nov 2007.

[9] R. Want, “An introduction to RFID technology,” IEEE Pervasive Computing, vol. 5, no. 1, pp. 25–33, Jan 2006.

[10] S. Gezici, Z. Tian, G. B. Giannakis, H. Kobayashi, A. F. Molisch, H. V. Poor, and Z. Sahinoglu, “Localization via ultra-wideband radios: a look at positioning aspects for future sensor networks,” IEEE Signal Processing Magazine, vol. 22, no. 4, pp. 70–84,
July 2005.

[11] R. Mautz and S. Tilch, “Survey of optical indoor positioning systems,” in Indoor Positioning and Indoor Navigation (IPIN), 2011 International Conference on, Sept 2011, pp. 1–7.

[12] M. S. Svalastog, “Indoor positioning-technologies, services and architectures,” Cand Sient Thesis, OSLO Department of Informatics, May 2007.

[13] Z. Farid, R. Nordin, and M. Ismail, “Recent advances in wireless indoor localization techniques and system,” Computer Networks and Communications, vol. 2013, 2013.

[14] G. Wang, A. M. C. So, and Y. Li, “Robust convex approximation methods for TDoA-based localization under nlos conditions,” IEEE Transactions on Signal Processing,
vol. 64, no. 13, pp. 3281–3296, July 2016.

[15] Z. Sun, R. Farley, T. Kaleas, J. Ellis, and K. Chikkappa, “Cortina: Collaborative contextaware indoor positioning employing RSS and RToF techniques,” in Pervasive Computing and Communications Workshops (PERCOM Workshops), 2011 IEEE International Conference on, March 2011, pp. 340–343.

[16] D. Zhai and Z. Lin, “RSS-based indoor positioning with biased estimator and local geographical factor,” in Telecommunications (ICT), 2015 22nd International Conference on, April 2015, pp. 398–402.

[17] I. Recommendations, “Propagation data and prediction methods for the planning of indoor radiocommunication systems and radio local area networks in the frequency range 900 MHz to 100 GHz,” ITU Recommendations, 2001.

[18] C. Koweerawong, K. Wipusitwarakun, and K. Kaemarungsi, “Indoor localization improvement via adaptive RSS fingerprinting database,” in The International Conference on Information Networking 2013 (ICOIN), Jan 2013, pp. 412–416.

[19] D. Li, B. Zhang, Z. Yao, and C. Li, “A feature scaling based k-nearest neighbor algorithm for indoor positioning system,” in 2014 IEEE Global Communications Conference, Dec 2014, pp. 436–441.

[20] D. T. Larose, “k-nearest neighbor algorithm,” Discovering Knowledge in Data: An Introduction to Data Mining, pp. 90–106, 2005.

[21] N. Uchitomi, A. Inada, M. Fujimoto, T. Wada, K. Mutsuura, and H. Okada, “Accurate indoor position estimation by swift-communication range recognition (S-CRR) method in passive rfid systems,” in Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on, Sept 2010, pp. 1–7.

[22] M. M. Pietrzyk and T. von der Grn, “Experimental validation of a TOA UWB ranging platform with the energy detection receiver,” in Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on, Sept 2010, pp. 1–8.

[23] G. Fischer, O. Klymenko, D. Martynenko, and H. Luediger, “An impulse radio UWB transceiver with high-precision TOA measurement unit,” in Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on, Sept 2010, pp. 1–8.

[24] M. Segura, H. Hashemi, C. Sisterna, and V. Mut, “Experimental demonstration of selflocalized ultra wideband indoor mobile robot navigation system,” in Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on, Sept 2010, pp. 1–9.

[25] H. M. Khoury and V. R. Kamat, “Evaluation of position tracking technologies for user localization in indoor construction environments,” Automation in Construction, vol. 18, no. 4, pp. 444–457, 2009.

[26] A. Cazzorla, G. D. Angelis, A. Moschitta, M. Dionigi, F. Alimenti, and P. Carbone, “A 5.6-ghz UWB position measurement system,” IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 3, pp. 675–683, March 2013.

[27] S. Mazuelas, A. Bahillo, R. M. Lorenzo, P. Fernandez, F. A. Lago, E. Garcia, J. Blas, and E. J. Abril, “Robust indoor positioning provided by real-time RSSI values in unmodified WLAN networks,” IEEE Journal of Selected Topics in Signal Processing, vol. 3, no. 5, pp. 821–831, Oct 2009.

[28] M. A. Youssef, A. Agrawala, and A. U. Shankar, “WLAN location determination via clustering and probability distributions,” in Pervasive Computing and Communications, 2003. (PerCom 2003). Proceedings of the First IEEE International Conference on, March 2003, pp. 143–150.

[29] T. King, S. Kopf, T. Haenselmann, C. Lubberger, andW. Effelsberg, “Compass: A probabilistic indoor positioning system based on 802.11 and digital compasses,” in the First Association for Computing Machinery International Workshop Conf. Wireless network
testbeds, experimental evaluation & characterization, 2006, pp. 34–40.

[30] L. Koski, T. Perl, and R. Pich, “Indoor positioning using WLAN coverage area estimates,” in Indoor Positioning and Indoor Navigation (IPIN), 2010 International Conference on, Sept 2010, pp. 1–7.

[31] M. Baum, B. Niemann, F. Abelbeck, D. H. Fricke, and L. Overmeyer, “Qualification tests of HF RFID foil transponders for a vehicle guidance system,” in 2007 IEEE Intelligent Transportation Systems Conference, Sept 2007, pp. 950–955.

[32] S.W. Smith et al., “The scientist and engineer’s guide to digital signal processing,” 1997.

[33] “Simplelink ultra-low power wireless MCU for Bluetooth low energy,” Texas Instruments, Datasheet CC2640, 2015, rev. B. [Online]. Available: http://www.ti.com/product/CC2640/description.

[34] Allegro.cc. [Online]. Available: https://www.allegro.cc/.

[35] Allegro.cc. [Online]. Available: https://www.allegro.cc/manual/4/.
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
無相關期刊