Advances in UWB-based Indoor Position Estimation and its Application in Fall Detection

In an indoor propagation environment, the position of an Object of
Interest (OOI) is typically estimated by cleverly manipulating range
or proximity measurements that are obtained from a series of reference
node combinations. In a noise-free propagation scenario, these
measured parameters are fed into conventional position estimation
techniques and an accurate estimate of the OOI’s position is obtained.
In practice, the propagation scenario is never quite noise-free; hence
the OOI’s position estimate is obtained in error. Ultra-Wideband
(UWB) is a wireless communication technology that is able to resolve
individual multipath components and this ensures that it is capable
of estimating the arrival time of the first signal path. The implication
of this lies in the fact that the accuracy of the range or proximity
measurements obtained from the reference node combinations is guaranteed;
hence leading to a reliable estimate of the OOI’s position.
In the research work presented in this thesis, the body of knowledge
that relates to indoor position estimation is advanced upon. With a
primary focus of enhancing the estimation accuracy of indoor position
estimation systems, UWB is utilised as the underlying wireless
communications technology. The challenges faced by current UWBbased
position estimation systems are identified and tackled directly.
Specifically, the position estimation error that is due to multipath
propagation is addressed and a pre-localisation algorithm that serves
the purpose of resolving individual multipath UWB signals in the
immediate environment is proposed.
Additionally, a novel position estimation technique coined as Time
Reflection of Arrival (TROA) is presented in this thesis. Through a
series of Mean Squared Error (MSE) and Cram´er-Rao Lower Bound
(CRLB) analyses, TROA is shown to be very effective when compared
to TOA and the typically unvoiced TSOA technique. In the last section
of this thesis, an application of UWB in the area of Biomedical
Engineering is demonstrated. Specifically, UWB-based position estimation
is used to define a novel fall detection algorithm tailored for
Dementia patients.