NLOS Identification via AdaBoost for Wireless Network Localization

Abstract

To identify NLOS propagations for a high-resolution wireless localization system, a strong NLOS classifier is proposed via a machine learning algorithm named AdaBoost. Signal features are extracted to characterize the distinctions between LOS and NLOS, based on which a series of weak learners are primarily given. By training and re-sampling data iteratively, weak learners with the minimal classification error are selected and the weighted sum is boosted into a strong classifier. Simulations demonstrate that the proposed AdaBoost-based NLOS classifier can improve NLOS identification performance, leading to reduced positioning errors.