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Research on Indoor Passive Positioning Technology Based on WiFi

Lei Sun1, Ling Tan1,*, Wenjie Ma1, Jingming Xia2

1 School of Computer and Software, Nanjing University of Information Science & Technology, Nanjing, China
2 School of Electronic & Information Engineering, Nanjing University of Information Science & Technology, Nanjing, China

* Corresponding Author: Ling Tan. Email:

Journal on Internet of Things 2020, 2(1), 23-35.


In recent years, WiFi indoor positioning technology has become a hot research topic at home and abroad. However, at present, indoor positioning technology still has many problems in terms of practicability and stability, which seriously affects the accuracy of indoor positioning and increases the complexity of the calculation process. Aiming at the instability of RSS and the more complicated data processing, this paper proposes a low-frequency filtering method based on fast data convergence. Low-frequency filtering uses MATLAB for data fitting to filter out low-frequency data; data convergence combines the mean and multi-data parallel analysis process to achieve a good balance between data volume and system performance. At the same time, this paper combines the position fingerprint and the relative position method in the algorithm, which reduces the error on the algorithm system. The test results show that the strategy can meet the requirements of indoor passive positioning and avoid a large amount of data collection and processing, and the average positioning error is below 0.5 meters.


Cite This Article

L. Sun, L. Tan, W. Ma and J. Xia, "Research on indoor passive positioning technology based on wifi," Journal on Internet of Things, vol. 2, no.1, pp. 23–35, 2020.

This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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