  Precise indoor localization of moving targets is a challenging activity which
cannot be easily accomplished without combining different sources of
information. In this sense, the combination of different data sources with an
appropriate filter might improve both positioning and tracking performance.
This work proposes an algorithm for hybrid positioning in Wireless Sensor
Networks based on data fusion of UWB and inertial information. A constant-gain
Steady State Kalman Filter is used to bound the complexity of the system,
simplifying its implementation on a typical low-power WSN node. The performance
of the presented data fusion algorithm has been evaluated in a realistic
scenario using both simulations and realistic datasets. The obtained results
prove the validity of this approach, which efficiently fuses different
positioning data sources, reducing the localization error.
