Indoor Localization Enhancement Based on Time of Arrival Using Sectoring Method

Автор: Ahmed K. Daraj, Alhamzah T. Mohammad, Mahmood F. Mosleh

Журнал: International Journal of Intelligent Systems and Applications @ijisa

Статья в выпуске: 3 vol.12, 2020 года.

Бесплатный доступ

The indoor wireless communication in general, suffers from several challenges like, signal reflection, diffraction, and attenuation. With these problems, the error range is increased significantly and the accuracy will be lost. To address those problems, Mini Zone (MZ)e technique propos in this paper which aim to partition building into small areas lead to more simplicity and flexibility to assign suitable parameters for specific area rather than whole building. To do that, case study building separated to seven zone (A-G). Each zone has its specific characteristics related to its contents such as, objects, walls, windows and any types of materials in addition to the distance between transmitters and each zone. We took in account these specific parameters to estimate the correct position. 56 receivers (8 for each zone) and 3 transmitters deployed in the case study building. The Wireless Insite Package has been used to design the chosen building and measure the required parameters. The target position has been estimated depending on RSS and ToA methods The objectives of this study are to implement a dynamic system that has capabilities to estimate position under deference conditions like LOS or NLO with the same accuracy. In addition, study the suitability of TOA and RSS methods to estimate position. These objectives were done based on the proposed technique by decrease error in the whole system to an acceptable level to be (0.293502m). Also, the results confirm that the TOA method was better than RSS by using propos technique.

Еще

Mini Zone, multipath, indoor localization, TOA, RSS

Короткий адрес: https://sciup.org/15017497

IDR: 15017497   |   DOI: 10.5815/ijisa.2020.03.01

Список литературы Indoor Localization Enhancement Based on Time of Arrival Using Sectoring Method

  • Zekavat, Reza, and R. Michael Buehrer. Handbook of position location: Theory, practice and advances. 1st ed., Vol. 27. John Wiley & Sons, 2011.
  • B. Jia, B. Huang, H. Gao, W. Li, and L. Hao, “Selecting Critical Wi-Fi APs for Indoor Localization Based on a Theoretical Error Analysis,” IEEE Access, vol. 7, pp. 36312–36321, 2019.
  • C. Chen, Y. Chen, H. Q. Lai, Y. Han, and K. J. R. Liu, “High accuracy indoor localization: A Wi-Fi based approach,” ICASSP, IEEE Int. Conf. Acoust. Speech Signal Process. - Proc., vol. 2016-May, pp. 6245–6249, 2016.
  • B. Kim, W. Bong, and Y. C. Kim, “Indoor localizationfor Wi-Fi devices by cross-monitoring AP and weighted triangulation,” 2011 IEEE Consum. Commun. Netw. Conf. CCNC’2011, pp. 933–936, 2011.
  • H. Chih, Y. Chen, T. Juang, and Yi-Ting Wu. "An adaptive wi-fi indoor localization scheme using deep learning." In 2018 IEEE Asia-Pacific Conference on Antennas and Propagation (APCAP), pp. 132-133. IEEE, 2018
  • S. A. Yuvaraj, and L. C. Siddanna, “High performance implementation of RSSI based Wi-Fi location tracker for android applications,” J. Theor. Appl. Inf. Technol., vol. 71, no. 1, pp. 97–106, 2015.
  • P. Jiang, Y. Zhang, W. Fu, H. Liu, and X. Su, “Indoor mobile localization based on Wi-Fi fingerprint’s important access point,” Int. J. Distrib. Sens. Networks, vol. 2015, 2015.
  • E. Navarro, B. Peuker, M. Quan, A. C. Clark, and J. Jipson, “Wi-Fi Localization Using RSSI Fingerprinting,” pp. 1–6, 2010.
  • Z. E. Khatab, A. Hajihoseini, and S. A. Ghorashi, “A Fingerprint Method for Indoor Localization Using Autoencoder Based Deep Extreme Learning Machine,” IEEE Sensors Lett., vol. 2, no. 1, pp. 1–4, 2017.
  • M. M. Abdulwahid, O. A. Shareef, M. F. Mosleh, and R. A. Abd-Alhmeed “A Comparison between Different C-band and mmWave band Frequencies for Indoor Communication,” Journal of Communications, vol. 10, no. 3, pp. 5663–5679, 2019.
  • M. El Hajj, G. Zaharia, G. El Zein, H. Farhat, and S. Sadek, “Millimeter-Wave Propagation Measurements at 60 GHz in Indoor Environments,” 2019 Int. Symp. Signals, Circuits Syst., pp. 1–4, 2019.
  • Z. Lin, T. Lv, and P. T. Mathiopoulos, “3-D Indoor Positioning for Millimeter-Wave Massive MIMO Systems,” IEEE Trans. Commun., vol. 66, no. 6, pp. 2472–2486, 2018.
  • I. A. Hemadeh, K. Satyanarayana, M. El-Hajjar, and L. Hanzo, “Millimeter-Wave Communications: Physical Channel Models, Design Considerations, Antenna Constructions, and Link-Budget,” IEEE Commun. Surv. Tutorials, vol. 20, no. 2, pp. 870–913, 2018.
  • Akiyama, Takayuki, Masanori Sugimoto, and Hiromichi Hashizume."Time-of-arrival-based smartphone localiza-tion using visible light communication." In 2017 International Conference on Indoor Positioning and Indoor Navigation (IPIN), pp. 1-7. IEEE, 2017.
  • Y. Hou, X. Yang, and Q. H. Abbasi, “Efficient AoA-Based Wireless Indoor Localization for Hospital Outpatients Using Mobile Devices,” Sensors (Basel)., vol. 18, no. 11, pp. 1–17, 2018.
  • S. Sadowski and P. Spachos, “RSSI-Based Indoor Localization with the Internet of Things,” IEEE Access, vol. 6, pp. 30149–30161, 2018.
  • O. A. S. Al-ani, K. S. Muttair, and M. F. Mosleh, “Outdoor Transmitter Localization using Multiscale Algorithm,” International Journal of Simulation Systems, Science & Technology., pp. 1–7, 2019.
  • H. Xia, J. Zuo, S. Liu, and Y. Qiao, “Indoor Localization on Smartphones Using Built-In Sensors and Map Constraints,” IEEE Trans. Instrum. Meas., vol. 68, no. 4, pp. 1189–1198, 2019.
  • S. Wu, S. Zhang, and D. Huang, “A TOA-Based Localization Algorithm with Simultaneous NLOS Mitigation and Synchronization Error Elimination,” IEEE Sensors Lett., vol. 3, no. 3, pp. 1–4, 2019.
  • B. Al-Qudsi, M. El-Shennawy, Y. Wu, N. Joram, and F. Ellinger, “A hybrid TDoA/RSSI model for mitigating NLOS errors in FMCW based indoor positioning systems,” 2015 11th Conf. Ph.D. Res. Microelectron. Electron. PRIME 2015., no. 1, pp. 93–96, 2015.
  • H. Cho, J. Ji, Z. Chen, H. Park, and W. Lee, “Accurate Distance Estimation between Things: A Self-correcting Approach,” Open J. Internet Things, vol. 1, no. 2, pp. 19–27, 2015.
  • S. Tomic, M. Beko, M. Tuba, and V. M. F. Correia, “Target Localization in NLOS Environments Using RSS and TOA Measurements,” IEEE Wirel. Commun. Lett., vol. 7, no. 6, pp. 1062–1065, 2018.
  • S. Tomic and M. Beko, “Target localization via integrated and segregated ranging based on RSS and TOA measurements,” Sensors (Switzerland), vol. 19, no. 2, pp. 1–20, 2019.
  • S. Tomic, M. Beko, R. Oliveira, L. Bernardo, N. Bacanin, and M. Tuba, “On Hybrid RSS/TOA Target Localization in NLOS Environments,” 2018 14th Int. Wirel. Commun. Mob. Comput. Conf. IWCMC 2018, pp. 1471–1476, 2018.
  • S. Chang, Y. Li, X. Yang, H. Wang, W. Hu, and Y. Wu, “A novel localization method based on RSS-AOA combined measurements by using polarized identity,” IEEE Sens. J., vol. 19, no. 4, pp. 1463–1470, 2019.
  • S. Tiwari et al., “Practical result of wireless indoor position estimation by using hybrid TDOA/RSS algorithm,” Can. Conf. Electr. Comput. Eng., no. 1, pp. 1–5, 2010.
  • D. Wu, Y. Xu, and L. Ma, “Research on RSS based indoor location method,” KESE 2009 - 2009 Pacific-Asia Conf. Knowl. Eng. Softw. Eng., pp. 205–208, 2009.
  • S. Tomic and M. Beko, “A robust NLOS bias mitigation technique for RSS-TOA-based target localization,” IEEE Signal Process. Lett., vol. 26, no. 1, pp. 64–68, 2019.
  • Y. Wang, X. Yang, Y. Zhao, Y. Liu, and L. Cuthbert, “Bluetooth positioning using RSSI and triangulation methods,” 2013 IEEE 10th Consum. Commun. Netw. Conf. CCNC 2013, pp. 837–842, 2013.
  • M. Shchekotov, “Indoor Localization Method Based on Wi-Fi Trilateration Technique,” Proceeding 16Th Conf. Fruct Assoc., pp. 177–179, 2014.
  • ITU-R, Effects of building materials and structures on radiowave propagation above about 100 MHz P Series Radiowave propagation, Recomm. ITU-R P.2040, vol. 1, 2013.
Еще
Статья научная