Non-Line-of-Sight Identification for UWB Indoor Positioning Systems using Support Vector Machines

Abstract

This paper presents a Non-Line-Of-Sight (NLOS) identification approach based on machine learning algorithms for ultra wide band positioning systems. The identification of NLOS conditions is crucial for positioning using trilateration as NLOS introduces positive biases in the calculated distances. The proposed method is based on the classification of the Channel Impulse Responses using Fisher’s Linear Discriminant and Support Vector Machines (SVM). The proposed approach has been validated by measurements in both an anechoic chamber where known reflections and obstacles are introduced and in a basement corridor as real environment scenario with more than 500 and 700 measured data sets for training, respectively. Results show an average identification accuracy of 92% for the case using SVM in the anechoic chamber and almost 100% for Fisher’s discriminant combined with SVM for the corridor scenario.