LoRaCompass: Robust Reinforcement Learning to Efficiently Search for a LoRa Tag

2025-11-14

AI Summary

To address cascading decision errors and accuracy degradation in LoRa tag localization under unknown environments—caused by RSSI signal fluctuations and domain shift—this paper proposes LoRaCompass, a reinforcement learning-based sequential localization framework. Methodologically, it operates without maps or prior knowledge, relying solely on RSSI measurements and supporting both ground and UAV platforms. Its key contributions are: (1) a spatially aware feature extractor coupled with a policy distillation loss to learn robust spatial representations; and (2) a UCB-inspired exploration mechanism that enhances search stability across domains and under noise. Evaluated in previously unseen environments spanning over 80 km², LoRaCompass achieves a localization success rate exceeding 90%. Within 100 meters, its search efficiency improves by 40% over state-of-the-art methods, and the search path length scales approximately linearly with the initial distance to the target.

Technology Category

• Search and Optimization: Learning to Search
• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: ML for personalized search and recommendations

Abstract

The Long-Range (LoRa) protocol, known for its extensive range and low power, has increasingly been adopted in tags worn by mentally incapacitated persons (MIPs) and others at risk of going missing. We study the sequential decision-making process for a mobile sensor to locate a periodically broadcasting LoRa tag with the fewest moves (hops) in general, unknown environments, guided by the received signal strength indicator (RSSI). While existing methods leverage reinforcement learning for search, they remain vulnerable to domain shift and signal fluctuation, resulting in cascading decision errors that culminate in substantial localization inaccuracies. To bridge this gap, we propose LoRaCompass, a reinforcement learning model designed to achieve robust and efficient search for a LoRa tag. For exploitation under domain shift and signal fluctuation, LoRaCompass learns a robust spatial representation from RSSI to maximize the probability of moving closer to a tag, via a spatially-aware feature extractor and a policy distillation loss function. It further introduces an exploration function inspired by the upper confidence bound (UCB) that guides the sensor toward the tag with increasing confidence. We have validated LoRaCompass in ground-based and drone-assisted scenarios within diverse unseen environments covering an area of over 80km^2. It has demonstrated high success rate (>90%) in locating the tag within 100m proximity (a 40% improvement over existing methods) and high efficiency with a search path length (in hops) that scales linearly with the initial distance.

Problem

• Locating missing persons' LoRa tags efficiently in unknown environments
• Overcoming domain shift and signal fluctuation in reinforcement learning
• Reducing search path length while maintaining high localization accuracy

Innovation

• Reinforcement learning for robust LoRa tag search
• Spatial feature extractor with policy distillation
• UCB-inspired exploration for confident tag localization