Dynamic Accuracy Estimation in a Wi-Fi-based Positioning System

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.

TDOA-TWR based positioning algorithm for UWB localization system

Ultra-wideband positioning systems intended for indoor applications often work in non-line of sight conditions, which result in insufficient precision and accuracy of derived localizations. One of the possible solutions is the implementation of cooperative positioning techniques. The following paper describes a cooperative ultra-wideband positioning system which calculates tag position from TDOA and distance between tags measurements. In the paper positioning system architecture is described and an exemplary transmission scheme for cooperative systems is presented. Considered localization system utilizes an Extended Kalman Filter based algorithm. The algorithm was investigated with simulations and experiments. Conducted experiment consisted in fusing results gathered from typical TDOA positioning system infrastructure and ranging results obtained with ultra-wideband radio modules. The research has shown that the use presented cooperative algorithm increases positioning precision.