Unsupervised Symbolization with Adaptive Features for LoRa-Based Localization and Tracking

While LoRa overcomes the high-power consumption and deployment costs of GPS and mobile networks, it faces challenges in accuracy. This paper presents a method for LoRa-based localization and tracking. It uses unsupervised symbolization to analyze received signal features. We use partitioning, D-Markov machines for symbolization and the Chinese restaurant process to achieve unsupervised symbolization. In particular, a novel adaptive feature extraction technique is proposed in partitioning to overcome the problems of over-tracking and under-tracking. Mean spectral kurtosis analysis is performed across several partitioning techniques to assess their symbolization effectiveness. This enables the selection of the most appropriate partitioning technique. This enhances the localization and tracking accuracy of target objects by focusing on robustness to noise and multipath effects. The proposed method learns and estimates the distance range simultaneously, thereby eliminating the need for a separate offline training phase and the storage of reference coordinates. Experimental results using LoRa highlight the proposed method's efficacy in real-time localization, tracking, and superiority over the state-of-the-art method.