Hybrid Positioning through Extended Kalman Filter withInertial Data Fusion

In wireless sensor networks (WSNs), hybridalgorithms are widely used in order to improve the finalpositioning accuracy. This paper presents a hybrid positioningalgorithm which combines time of arrival (TOA) and receivedsignal strength (RSS) measurements using two different radiotechnologies, ultra wide band (UWB) and ZigBee, respectively.The TOA measurements are used to estimate the distancesbetween a mobile node and a set of anchor nodes. Both UWBbaseddistance estimates and RSS measurements based onZigBee are simultaneously processed by an Extended KalmanFilter (EKF). Moreover, a low cost inertial device is also usedto acquire acceleration measurements which proved to beuseful in order to detect the motion of the mobile node. Thisinformation has also been integrated in the EKF algorithmaccordingly. The performance of the final hybrid positioningalgorithm is compared with the conventional EKF which uses asingle type of range measurements, TOA or RSS. Simulationresults based on a real measurements campaign, show that thehybrid algorithm significantly improves positioning accuracy.In addition, a further improvement has been achieved byapplying the motion detection approach based on inertialmeasurements performed by the low cost acceleration sensor.