Hybrid Positioning through Extended Kalman Filter withInertial Data Fusion

Abstract

In wireless sensor networks (WSNs), hybrid algorithms are widely used in order to improve the final positioning accuracy. This paper presents a hybrid positioning algorithm which combines time of arrival (TOA) and received signal strength (RSS) measurements using two different radio technologies, ultra wide band (UWB) and ZigBee, respectively. The TOA measurements are used to estimate the distances between a mobile node and a set of anchor nodes. Both UWB- based distance estimates and RSS measurements based on ZigBee are simultaneously processed by an Extended Kalman Filter (EKF). Moreover, a low cost inertial device is also used to acquire acceleration measurements which proved to be useful in order to detect the motion of the mobile node. This information has also been integrated in the EKF algorithm accordingly. The performance of the final hybrid positioning algorithm is compared with the conventional EKF which uses a single type of range measurements, TOA or RSS. Simulation results based on a real measurements campaign, show that the hybrid algorithm significantly improves positioning accuracy. In addition, a further improvement has been achieved by applying the motion detection approach based on inertial measurements performed by the low cost acceleration sensor.