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Evaluation of Weighted Impulse Radio for Ultra-Wideband Localization

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Abstract

This research paper presents indoor localization using weighted localization algorithm (WLA) with impulse radio for ultra-windband. The ultra-wide band is wireless technology short range system, especially in, an indoor localization. In this paper, the proposed process consists of two parts: Firstly, a data collected by measurement environment in a Line-of-Sight using impulse radio and secondly extension weighted localization algorithm with wireless ultra-wideband (WUWB) for localization short-range system in an indoor environment. Its can be improve the distance error. The results are evaluated by the cumulative distribution function to check a probability of distance error. The results provided good improvement which increase the wireless indoor localization precision less than 1 m. The proposed WLA for WUWB localization can be used in various applications for the wireless indoor environment.

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References

  1. Uradzinski, M., Guo, H., Liu, X., & Yu, M. (2017). Advanced indoor positioning using zigbee wireless technology. Wireless Personal Communications, 97(4), 6509–6518.

    Article  Google Scholar 

  2. Nikookar, H., & Prasad, R. (2008). Introduction to ultra wideband for wireless communications. Berlin: Springer.

    Google Scholar 

  3. Promwong, S. (2009). Study on waveform distortion due to the antenna in ultra wideband impulse radio. In 2009 9th International Symposium on Communications and Information Technology. Incheon, Korea.

  4. Bulusu, N., Heidemann, J., & Estrin, D. (2000). GPS-less low-cost outdoor localization for very small devices. Personal Communications IEEE, 7, 28–34.

    Article  Google Scholar 

  5. Salvadori, F., De Campos, M., Sausen, P., De Camargo, R., Gehrke, C., Rech, C., et al. (2009). Monitoring in industrial systemsusing wireless sensor network with dynamic power management. IEEE Transactions on Instrumentation and Measurement, 58(9), 3104–3111.

    Article  Google Scholar 

  6. Federal Communications Commission (FCC). (2002). Revision of Part 15 of the Commissions Rules Regarding Ultra-Wideband Transmission Systems, First Report and Order, FCC 02-48.

  7. Akyildiz, I., Su, W., Sankarasubramaniam, Y., & Cayirci, E. (2002). A survey on sensor networks. IEEE Communications Magazine, 40(8), 102–114.

    Article  Google Scholar 

  8. The Network Simulator—ns-2. (2012). from http://www.isi.edu/nsnam/ns/.

  9. Wang, Y., Fang, S., Cao, Y., & Sun, H. (2009). Image-based exploration obstacle avoidance for mobile robot. In Proceedings of IEEE Chinese control decision conference (pp. 3019–3023).

  10. Thongkam, J., Sangthong, J., Supanakoon, P., & Promwong, S. (2012). Evaluation of wireless sensor network with weighted centroid localization algorithm based on measurement data. In 2012 international conference on engineering, applied sciences, and technology (ICEAST), Bangkok, Thailand.

  11. Thongkam, J., Sangthong, J., Supanakoon, P., & Promwong, S. (2013). Evaluation of ZigBee sensor network localization by using relative-span exponentially weighted localization algorithm. In 2013 international conference on embedded system and intelligent technology (ICESIT), Nong Khai, Thailand.

  12. DeSouza, G. N., & Kak, A. C. (2002). Vision for mobile robot navigation: A survey. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(2), 237–267.

    Article  Google Scholar 

  13. Kim, K., Jung, B., Lee, W., & Du, D. Z. (2011). Adaptive path planning for randomly deployed wireless sensor networks. Journal of Information Science and Engineering, 27(3), 1091–1106.

    Google Scholar 

  14. Li, X., Mitton, N., Simplot-Ryl, I., & Simplot-Ryl, D. (2012). Dynamic beacon mobility scheduling for sensor localization. IEEE Transactions on Parallel and Distributed Systems, 23(8), 1439–1452.

    Article  MATH  Google Scholar 

  15. Shalaby, M., Shokair, M., & Messiha, N. W. (2019). Performance of RSS based cooperative localization in millimeter wave wireless sensor networks. Wireless Personal Communications, 109(3), 1955–1970.

    Article  Google Scholar 

  16. Li, X. J., & Bharanidharan, M. (2019). RSSI fingerprinting based iPhone indoor localization system without apple API. Wireless Personal Communications, 1–14.

  17. Kumar, S. (2019). Performance analysis of RSS-based localization in wireless sensor networks. Wireless Personal Communications, 108(2), 769–783.

    Article  Google Scholar 

  18. Danaee, M. R., & Behnia, F. (2020). Improved least squares approaches for differential received signal strength-based localization with unknown transmit power. Wireless Personal Communications, 110(3), 1373–1401.

    Article  Google Scholar 

  19. Denkovski, D., Angjelichinoski, M., Atanasovski, V., & Gavrilovska, L. (2014). RSS-based self-localization framework for future wireless networks. Wireless Personal Communications, 78(3), 1755–1776.

    Article  MATH  Google Scholar 

  20. Wu, S., Xu, D., & Liu, S. (2013). Weighted linear least square localization algorithms for received signal strength. Wireless Personal Communications, 72(1), 747–757.

    Article  Google Scholar 

  21. Thaljaoui, A., & El Khediri, S. (2019). Adopting dilution of precision for indoor localization. Wireless Personal Communications, 1–34.

  22. Petric, M., Neskovic, A., Neskovic, N., & Borenovic, M. (2019). Indoor localization using multi-operator public land mobile networks and support vector machine learning algorithms. Wireless Personal Communications, 104(4), 1573–1597.

    Article  Google Scholar 

  23. Lv, Y., Liu, W., Wang, Z., & Zhang, Z. (2020). WSN localization technology based on hybrid GA-PSO-BP algorithm for indoor three-dimensional space. Wireless Personal Communications.

  24. Pirzada, N., Nayan, M. Y., Subhan, F., Abro, A., Hassan, M. F., & Sakidin, H. (2017). Location fingerprinting technique for WLAN device-free indoor localization system. Wireless Personal Communications, 95(2), 445–455.

    Article  Google Scholar 

  25. Gomez, J., Tayebi, A., del Corte, A., Gutierrez, O., Gomez, J. M., & De Adana, F. S. (2013). A comparative study of localization methods in indoor environments. Wireless Personal Communications, 72(4), 2931–2944.

    Article  Google Scholar 

  26. Singh, S. P., & Sharma, S. C. (2018). A PSO based improved localization algorithm for wireless sensor network. Wireless Personal Communications, 98(1), 487–503.

    Article  Google Scholar 

  27. Harikrishnan, R., Kumar, V. J. S., & Ponmalar, P. S. (2016). A comparative analysis of intelligent algorithms for localization in wireless sensor networks. Wireless Personal Communications, 87(3), 1057–1069.

    Article  Google Scholar 

  28. Goyal, S., & Patterh, M. S. (2014). Wireless sensor network localization based on cuckoo search algorithm. Wireless Personal Communications, 79(1), 223–234.

    Article  Google Scholar 

  29. Rashid, H., & Turuk, A. K. (2013). Localization of wireless sensor networks using a single anchor node. Wireless Personal Communications, 72(2), 975–986.

    Article  Google Scholar 

  30. Banihashemian, S. S., Adibnia, F., & Sarram, M. A. (2018). A new range-free and storage-efficient localization algorithm using neural networks in wireless sensor networks. Wireless Personal Communications, 98(1), 1547–1568.

    Article  Google Scholar 

  31. Sharma, G., & Kumar, A. (2017). Dynamic range normal bisector localization algorithm for wireless sensor networks. Wireless Personal Communications, 97(3), 4529–4549.

    Article  Google Scholar 

  32. Rai, S., & Varma, S. (2017). Localization in wireless sensor networks using rigid graphs: A review. Wireless Personal Communications, 96(3), 4467–4484.

    Article  Google Scholar 

  33. Singh, M., & Khilar, P. M. (2017). A range free geometric technique for localization of wireless sensor network (WSN) based on controlled communication range. Wireless Personal Communications, 94(3), 1359–1385.

    Article  Google Scholar 

  34. Shi, Q., Huo, H., Fang, T., & Li, D. (2010). A distributed node localization scheme for wireless sensor networks. Wireless Personal Communications, 53(1), 15–33.

    Article  Google Scholar 

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Authors and Affiliations

  1. Faculty of Engineering, King Mongkut’s Institute of Technology Ladkrabang, Bangkok, Thailand

    Sathaporn Promwong & Jutamas Thongkam

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  1. Sathaporn Promwong
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  2. Jutamas Thongkam
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Correspondence to Sathaporn Promwong.

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Promwong, S., Thongkam, J. Evaluation of Weighted Impulse Radio for Ultra-Wideband Localization. Wireless Pers Commun 115, 2695–2704 (2020). https://doi.org/10.1007/s11277-020-07555-0

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