@Article{jiot.2020.09075,
AUTHOR = {Lei Sun, Ling Tan, Wenjie Ma, Jingming Xia},
TITLE = {Research on Indoor Passive Positioning Technology Based on WiFi},
JOURNAL = {Journal on Internet of Things},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {23--35},
URL = {http://www.techscience.com/jiot/v2n1/39678},
ISSN = {2579-0080},
ABSTRACT = {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.},
DOI = {10.32604/jiot.2020.09075}
}