Abstract
Wireless localization is emerging as a critical service in Internet of Things (IoT) applications, with Long Range (LoRa) technology gaining considerable attention due to its cost-effectiveness and extended-range capabilities. Given these distinctive features, this paper studies LoRa-based localization systems, focusing on the maximum likelihood estimator (MLE) algorithm as a direct localization approach and evaluating fundamental performance limits. Specifically, we derive the Cramér-Rao lower bound (CRLB) to assess localization accuracy, covering both the direct MLE method and time difference of arrival (TDOA) techniques. Numerical results suggest that LoRa signals hold substantial promise for localization, achieving meter-level accuracy without requiring additional infrastructure beyond the existing LoRa network used for communication.