Hybrid Positioning through Extended Kalman Filter with Inertial Data Fusion
Keywords:
Hybrid Positioning, Extended Kalman Filter, TOA/RSS, Data fusion, Power measurement, Wireless Sensor Networks.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.
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