Ultra-wideband indoor localization method based on Kalman filtering and Taylor algorithm

Abstract

Ultra-wideband (UWB) has strong anti-interference capability, low power consumption and competitive price advantage, making it widely used in short-range Internet of Things(IoT) applications, particularly for positioning. However, there are still many problems in practical applications, such as clock synchronization between base stations and non-line of sight (NLOS). To solve the problem that NLOS errors caused by the complex indoor environment affects the positioning accuracy, this paper proposes a Kalman algorithm-assisted Taylor algorithm for indoor positioning. Firstly, the distance between the base station and the tag is measured by Symmetrical Double-Sided Two Way Ranging (SDS-TWR), which is filtered by Kalman filter to eliminate some of the errors, and the preliminary position estimation result is calculated as the initial value. Taylor algorithm uses the initial values calculation to achieve precise positioning. Results show that the method can effectively eliminate the interference of indoor complex environment to the positioning system and improve the positioning accuracy.

Comparative Analysis of Integrated Filtering Methods Using UWB Localization in Indoor Environment

“…The computer has a UWB localization software Pozyx-creator-controller-2.1.0. package installed [ 37 ]. Table 1 contains a list of all the components used in the experiment.…”