  Currently there is no standard indoor positioning system, similar to outdoor
GPS. However, WiFi signals have been used in a large number of proposals to
achieve the above positioning, many of which use machine learning to do so. But
what are the most commonly used techniques in machine learning? What accuracy
do they achieve? Where have they been tested? This article presents a
systematic review of works between 2019 and 2021 that use WiFi as the signal
for positioning and machine learning models to estimate indoor position. 64
papers have been identified as relevant, which have been systematically
analyzed for a better understanding of the current situation in different
aspects. The results show that indoor positioning based on WiFi trends use
neural network-based models, evaluated in empirical experiments. Despite this,
many works still conduct an assessment in small areas, which can influence the
goodness of the results presented.
