The detection and mitigation of Non-Line-of-
Sight (NLOS) signals are crucial for achieving the full potential
of UWB-based indoor positioning. In dense multipath industrial
environments, it was seen that using the power characteristics
of the received signal to identify NLOS conditions is effective
when tracking stationary objects but is insufficient for mobile
object tracking. Hence, machine learning classifiers utilizing
Multi-Layer Perceptron (MLP) and Boosted Decision Trees
(BDT) were developed to improve NLOS detection. Through
experimental results from tests in a factory scenario, it is shown
that BDT yields a higher accuracy of 87% as compared to the
79% obtained by the received power-based method.
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Funding Info:
This work has been carried with funding provided under the Model Factory @ ARTC program; IAF-PP PG/20170607/005, CA/20180214/045