Improving UWB Based Indoor Positioning in Industrial Environments Through Machine Learning

Abstract

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.