High precise indoor positioning is the spotlight
for the new mobile generation 5G. Ultra-Wide Band (UWB)
technology stands out as the creditable preference for locating
the user in indoor scenarios. The principal limitation of this
technology appears in the coverage area that reaches a few tens
of meters. In our case of study, we have simulated a conceivable
real environment with UWB and Long Term Evolution (LTE)
base stations for positioning users. In this scenario, users have
been tracked by an Extended Kalman Filter (EKF), a memory
state filter to predict the movement of the user that improves the
performance of the system. In regions that receivers only track
isolated UWB stations we make use of this information in order
to improve the location provided by mobile networks. Essentially,
when performing trilateration using the data offered by LTE,
we also include the data of UWB in case that this information
do not serve to position by itself. In this manner, the coverage
area by at least one UWB station augments and accuracy of
the system improves in those regions where only LTE previously
provided location.
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