Dynamic Accuracy Estimation in a Wi-Fi-based Positioning System
This work addresses accuracy estimation for indoor positioning systems, which is incremental as it applies existing regression methods to a specific domain.
The paper tackled the problem of estimating localization errors in Wi-Fi-based indoor positioning by proposing a dynamic accuracy estimation method, achieving a mean absolute error of 0.72 m using random forest regression.
The paper presents a concept of a dynamic accuracy estimation method, in which the localization errors are derived based on the measurement results used by the positioning algorithm. The concept was verified experimentally in a Wi\nobreakdash-Fi based indoor positioning system, where several regression methods were tested (linear regression, random forest, k-nearest neighbors, and neural networks). The highest positioning error estimation accuracy was achieved for random forest regression, with a mean absolute error of 0.72 m.