To further improve the NLOS detection and mitigation performance for
Ultra-wideband (UWB) system, this thesis systematically investigates the
UWB LOS/NLOS errors. The LOS errors are evaluated in different
environments and with different distances. Different blockage materials
and blockage conditions are considered for NLOS errors. The UWB signal
propagation is also investigated. Furthermore, the relationships between
the CIRs and the accurate/inaccurate range measurements are
theoretically discussed in three different situations: ideal LOS path,
small-scale fading: multipath and NLOS path. These theoretical
relationships are validated with real measured CIRs in the Bosch
Shanghai office environment. Based on the error and signal propagation
investigation results, four different algorithms are proposed for four
different scenarios to improve the NLOS identification accuracy. After
the comparison of the localization performance for TOA/TDOA, it is found
that on normal office floor, TOA works better than TDOA. In harsh
industrial environments, where NLOS frequently occurs, TDOA is more
suitable than TOA. Thus, in the first scenario, the position estimation
is realized with TOA on the office floor, while in the second scenario,
a novel approach to combined TOA and TDOA with accurate range and range
difference selection is proposed in the harsh industrial environment.
The optimization of the feature combination and parameters in machine
learning algorithms for accurate measurement detection is discussed for
both scenarios. For the third and fourth scenarios, the UWB/IMU fusion
system stays in focus. Instead of detecting the NLOS outliers by
assuming that the error distributions are Gaussian, the accurate
measurement detection is realized based on the triangle inequality
theorem. All the proposed approaches are tested with the collected
measurements from the developed UWB system. The position estimation of
these approaches has better accuracy than that of the traditional
methods.
