Abstract
In recent years, RSSI-based indoor positioning system has been widely used worldwide due to its low installation cost and wide coverage. However, due to the complex and varied indoor environment, the crowd is relatively dense, and the propagation of wireless signals is greatly disturbed. This leads to the fact that the RSSI-based indoor positioning cannot meet the requirements of people. In this study a wireless signal compensation model considering population density is proposed. This model can use image information to compensate the path loss of RSS signals to achieve accurate indoor positioning.
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References
He S, Chan SHG (2015) Wi-Fi fingerprint-based indoor positioning: recent advances and comparisons. IEEE Commun Surv Tutor 2015:1
Zhang P, Zhao Q, Li Y, Niu X, Zhuang Y, Liu J (2015) Collaborative WiFi fingerprinting using sensor-based navigation on smartphones. Sensors 15:17534–17557
Wang Y, Yang X, Zhao Y, Liu Y, Cuthbert L (2013) Bluetooth positioning using RSSI and triangulation methods. In: Proceedings of the consumer communications and networking conference, Las Vegas, NV, USA
Aguirre E, Arpon J, Azpilicueta L, Falcone F (2012) Evaluation of electromagnetic dosimetry of wireless systems in complex indoor scenarios with human body interaction. Prog Electromagn Res B 43:189–209
Gosselin MC, Vermeeren G, Kuhn S, Kellerman V, Benkler S, Uusitupa TMI, Joseph W, Gati A, Wiart J, Meyer FJC et al (2011) Estimation formulas for the specific absorption rate in humans exposed to base-station antennas. IEEE Trans Electromagn Compat 53:909–922
Johnson J, Karpathy A, Fei-Fei L Densecap: fully convolutional localization networks for dense captioning
Mikolov T, Karafiát M, Burget L, Cernocký J, Khudanpur S (2010) Recurrent neural network based language model. In: Proceedings of the Interspeech, Makuhari, Chiba, Japan
Nguyen DT, Li W, Ogunbona PO (2016) Human detection from images and videos: a survey. Pattern Recognit 51:148–175
Acknowledgements
This work is supported by the National High Technology Research and Development Program of China (2012AA120802), National Natural Science Foundation of China (61771186), Postdoctoral Research Project of Heilongjiang Province (LBH-Q15121), Undergraduate University Project of Young Scientist Creative Talent of Heilongjiang Province (UNPYSCT-2017125), Postgraduate Innovative Research Project of Heilongjiang University (NO. YJSCX2019-059HLJU).
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Xu, G., Qin, D., Ji, P., Zhao, M., Guo, R., Feng, P. (2020). Research on Vision-Based RSSI Path Loss Compensation Algorithm. In: Liang, Q., Wang, W., Liu, X., Na, Z., Jia, M., Zhang, B. (eds) Communications, Signal Processing, and Systems. CSPS 2019. Lecture Notes in Electrical Engineering, vol 571. Springer, Singapore. https://doi.org/10.1007/978-981-13-9409-6_211
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DOI: https://doi.org/10.1007/978-981-13-9409-6_211
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