A Novel NLOS Acoustic Signal Identification Method for Indoor Localization Based on Machine Learning

Near-ultrasound acoustic localization has emerged as a cost-effective and precise technique for indoor localization. Acoustic Non-Line-of-Sight (NLOS) identification is essential in indoor localization systems. Current NLOS identification approaches predominantly utilize Matched Filter (MF) characteristics or signal spectrum output. However, these methods often fail to deliver adequate identification performance in cross-scenario environments, including those that employ statistical analysis and Deep Learning (DL) techniques. In this paper, we propose a novel NLOS identification method specifically designed for near-ultrasound localization signals. Our approach utilizes an enhanced Frequency-Modulated Continuous Wave (FMCW) technique to obtain an improved intermediate frequency (IF) signal. Subsequently, a subset of eight distinct features is extracted and selected from the IF signal. The features are then employed to enhance an XGBoost classifier for the identification of NLOS conditions. Experimental results across three scenarios demonstrate that our method attains a classification accuracy of 99.9% within identical-room settings, and achieves accuracies of 93.73% and 86.20% in cross-scenario environments, respectively. Additionally, the extracted features demonstrate promising performance not only within the presented XGBoost classifier but also across various statistical machine-learning models.