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Dergiler / Turkish Journal of Electrical Engineering and Computer Science / 2020 / Cilt: 28 - Sayı: 2

Accurate indoor positioning with ultra-wide band sensors

Taner ARSAN
Sayfa
1014–1029
Yayın Tarihi
2020-03-01
DOI
—
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⚡ TL;DR(1 Cümlede Hızlı Çıkarım)

Briefly, the average positioning error has been reduced from 16.34 cm to 7.43 cm.

📌 3 Maddede Bu Makale

🎯
Amaç & Hipotez:Ultra-wide band is one of the emerging indoor positioning technologies.
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Temel Bulgular & Veri:A test bed with the dimensions of 7.35 m × 5.41 m and 50 cm × 50 cm grids has been selected, and a total of 27,000 measurements have been collected from 180 test points.
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Sonuç & Katkı:Briefly, the average positioning error has been reduced from 16.34 cm to 7.43 cm.
🔬Tür:Modelleme & Sayısal Simülasyon
📍Kapsam:Genişletilmiş Akademik Örneklem
⚙️Değişkenler:Indoor positioning · ultra-wide band sensors · optimization

Abstract

Ultra-wide band is one of the emerging indoor positioning technologies. In the application phase, accuracy and interference are important criteria of indoor positioning systems. Not only the method used in positioning, but also the algorithms used in improving the accuracy is a key factor. In this paper, we tried to eliminate the effects of off-set and noise in the data of the ultra-wide band sensor-based indoor positioning system. For this purpose, optimization algorithms and filters have been applied to the raw data, and the accuracy has been improved. A test bed with the dimensions of 7.35 m × 5.41 m and 50 cm × 50 cm grids has been selected, and a total of 27,000 measurements have been collected from 180 test points. The average positioning error of this test bed is calculated as 16.34 cm. Then, several combinations of algorithms are applied to raw data. The combination of Big Bang-Big Crunch algorithm for optimization, and then the Kalman Filter have yielded the most accurate results. Briefly, the average positioning error has been reduced from 16.34 cm to 7.43 cm.

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Bu Makaleye Atıf Yap

ARSAN, T. (2020). Accurate indoor positioning with ultra-wide band sensors. Turkish Journal of Electrical Engineering and Computer Science, Cilt: 28 - Sayı: 2, 1014–1029. https://ajindex.com/articles/186612

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Kaynakça (6)

  1. Kaplan E, Hegarty C. Understanding GPS: Principles and Applications. Norwood, MA, USA: Artech House, 2008
  2. Hofmann-Wellenhof B, Lichtenegger H, Collins J. Global Positioning System: Theory and Practice. Wien, Austria: Springer, 2001
  3. Djuknic GM, Richton RE. Geolocation and assisted GPS. Computer 2001; 34(2): 123-125. doi: 10.1109/2.901174 [4] Huang H, Gartner G. A Survey of mobile indoor navigation systems. In: Gartner G, Ortag F (Editors). Cartography in Central and Eastern Europe. Springer: Heidelberg, Germany, 2010, 305-319
  4. Liu H, Darabi H, Banerjee P, Liu J. Survey of wireless indoor positioning techniques and systems. IEEE Transactions on Systems, Man, and Cybernetics, Part C Applications and Reviews 2007; 37 (6): 1067-1080. doi: 10.1109/TSMCC.2007.905750 [6] Gu YY, Lo A, Niemegeers I. A survey of indoor positioning systems for wireless personal networks. IEEE Communications Surveys and Tutorials 2009; 11 (1): 13-32. doi: 10.1109/SURV.2009.090103 [7] Ganick A, Ryan D. Method and system for modulating a light source in a light based positioning system using a DC bias. US Patent 2012; Patent No: US 8,334,901 B1
  5. Komine T, Nakagawa M. Fundamental analysis for visible-light communication system using LED lights. IEEE Transactions on Consumer Electronics 2004; 50 (1): 100-107. doi: 10.1109/TCE.2004.1277847 [9] Kumar N, Lourenco N, Spiez M, Aguiar R. Visible light communication systems conception and VIDAS. IETE Technical Review 2008; 25 (6): 359-367. doi: 10.4103/0256-4602.45428 [10] Shao WH, Zhao F, Wang C, Luo HY, Zahid TM et al. Location fingerprint extraction for magnetic field magnitude based indoor positioning. Journal of Sensors 2016; 1-16. doi: 10.1155/2016/1945695 [11] Galván-Tejada CE, García-Vázquez JP, Brena RF. Magnetic field feature extraction and selection for indoor location estimation. Sensors 2014; 14 (6): 11001-11015. doi: 10.3390/s140611001 [12] Gu Y, Chen M, Ren F, Li J. HED: Handling environmental dynamics in indoor WiFi fingerprint localization. In: Proceedings of the IEEE Wireless Communications and Networking Conference (WCNC); Doha, Qatar, 3-6 April 2016. pp. 1-6
  6. Brena RF, García-Vázquez JP, Galván-Tejada CE, Muñoz-Rodriguez D, Vargas-Rosales C et al. Evolution of indoor positioning technologies: A survey, Journal of Sensors 2017; 1-21. doi: 10.1155/2017/2630413 [14] Gezici S. A survey on wireless position estimation, Wireless Personal Communications 2008; 44 (3): 263-282. doi: 10.1007/s11277-007-9375-z