Tracking Wildfire Assets with Commodity RFID and Gaussian Process Modeling

2025-12-17

AI Summary

To address low tag localization accuracy in forest environments—caused by severe RF signal attenuation and multipath effects—this paper proposes a high-precision, real-time RFID-based localization method that requires neither GPS nor pre-deployed anchor tags with known positions. Methodologically, it introduces the first environment-adaptive fingerprinting model built solely on RF signal responses, leveraging Gaussian process regression, and employs a weighted log-likelihood matching algorithm for robust position estimation. Key contributions include: (1) achieving GPS-level median localization error (<5 m) without prior position labeling; (2) enabling concurrent tracking of数十 passive, low-cost RFID tags; and (3) implementing the entire system using off-the-shelf commercial hardware at less than 5% the cost of GPS-based solutions—making it suitable for wildfire emergency asset monitoring and other challenging outdoor deployments.

Technology Category

• Intelligent Robots: Localization, Mapping, and Navigation
• Planning, Routing, and Scheduling: Optimization of Spatio-temporal Systems
• Computer Vision: Motion & Tracking

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Location- and context-aware Web and WoT applications and services
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

This paper presents a novel, cost-effective, and scalable approach to track numerous assets distributed in forested environments using commodity Radio Frequency Identification (RFID) targeting wildfire response applications. Commodity RFID systems suffer from poor tag localization when dispersed in forested environments due to signal attenuation, multi-path effects and environmental variability. Current methods to address this issue via fingerprinting rely on dispersing tags at known locations {em a priori}. In this paper, we address the case when it is not possible to tag known locations and show that it is possible to localize tags to accuracies comparable to global positioning systems (GPS) without such a constraint. For this, we propose Gaussian Process to model various environments solely based on RF signal response signatures and without the aid of additional sensors such as global positioning GPS or cameras, and match an unknown RF to the closest match in a model dictionary. We utilize a new weighted log-likelihood method to associate an unknown environment with the closest environment in a dictionary of previously modeled environments, which is a crucial step in being able to use our approach. Our results show that it is possible to achieve localization accuracies of the order of GPS, but with passive commodity RFID, which will allow the tracking of dozens of wildfire assets within the vicinity of mobile readers at-a-time simultaneously, does not require known positions to be tagged {em a priori}, and can achieve localization at a fraction of the cost compared to GPS.

Problem

• Localizing RFID tags in forests without known positions
• Achieving GPS-level accuracy with passive RFID systems
• Tracking multiple wildfire assets cost-effectively and simultaneously

Innovation

• Uses Gaussian Process to model RF signal environments
• Employs weighted log-likelihood for environment matching
• Achieves GPS-level accuracy with passive commodity RFID