UWB localization using adaptive covariance Kalman Filter based on sensor fusion

Abstract

In recent years developments in medical engineering led to an increase in technical equipment and devices in the interventional suite. By providing a spatio-temporal identification and localization framework beneficial applications can be realized in a crowded, dynamic and harsh environment. Ultra wideband (UWB) using an impulse radio signaling scheme has attracted interest for applications where robust and precise localization is needed. By applying common tracking filters the precision of UWB localization can be further improved but it lacks in terms of dynamic behavior. In this paper we present a dynamically adapting covariance Kalman Filter based on sensor fusion of UWB localization and inertial measurements. We incorporate the absolute acceleration data from an inertial sensor to tune the state covariance matrix. Since both sensor results are separately used in the Kalman Filter no registration between the implemented sensors is needed. The implemented algorithm works well on experimental data from highly reflective medical environment and provides good results for static and dynamic measurement scenarios.