| Summary: | An accurate localization of unmanned aerial vehicles (UAVs) is crucial
for the execution of its growing applications such as surveillance and rescue
missions. Previous researches have extensively studied the usage of sensor
fusion algorithms to combine the sensors on board of the UAV to improve its
localization. However, application of collaborative localization techniques in
UAV navigation has not been investigated thus far. These novel algorithms
stand to improve the stability and accuracy of UAV localization approaches
through incorporation of additional sensors from other moving targets such
as an unmanned ground vehicle (UGV). It is believed that the accuracy of the
UAV localization will be further improved with help of multi-sensor Kalman
filter (MS-KF) and this collaborative sensor fusion approach leads to a better
accuracy than that of the single-sensor Kalman filter (SS-KF) approach. The
obtained results in this study show promising improvements of both position
and attitude with MS-KF. In comparison, the mean square error (MSE) for
position is 0.005 and 0.026 for the developed MS-KF and SS-KF, respectively.
Meanwhile, MSE for attitude is 2.396e-5 and 8.11e-4 for the developed MS-
KF and SS-KF, respectively. Based on these findings, the positive potential
of collaborative sensor fusion approach has been aptly highlighted.
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