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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Pascoal, R., José Manuel Naranjo Gómez, Alessandro Pinheiro & Mauro Cesar Cantarino Gil (2026). RESCUE-AR: Responsive Sensor-Fusion for Civil-Protection and Disaster Response Using Mobile Augmented Reality. Sensors,. https://doi.org/10.20944/preprints202609.0915.v1
Exportar Referência (IEEE) 
R. M. Pascoal et al.,  "RESCUE-AR: Responsive Sensor-Fusion for Civil-Protection and Disaster Response Using Mobile Augmented Reality", in Sensors, 2026
Exportar BibTeX 
@null{pascoal2026_1789866883791,
	year = "2026"
}
Exportar RIS 
TY  - GEN
TI  - RESCUE-AR: Responsive Sensor-Fusion for Civil-Protection and Disaster Response Using Mobile Augmented Reality
T2  - Sensors
AU  - Pascoal, R.
AU  - José Manuel Naranjo Gómez
AU  - Alessandro Pinheiro
AU  - Mauro Cesar Cantarino Gil
PY  - 2026
SN  - 1424-8220
DO  - 10.20944/preprints202609.0915.v1
AB  - Rapid situational awareness is a cornerstone of effective civil protection and disaster response, demanding highly resilient localization and decision-support capabilities under challenging environmental conditions. This paper presents RESCUE-AR (Responsive Sensor-Fusion for Civil-Protection and Disaster Response Using Mobile Augmented Reality), a conceptual and methodological framework for mobile Augmented Reality (AR) systems designed to support emergency operations through heterogeneous and adaptive sensor fusion. This work formalizes the mathematical and algorithmic architecture of the RESCUE-AR framework, establishing an adaptive multi-sensor measurement fusion strategy, integrating Visual SLAM, IMU, depth sensing, and GNSS data to enhance robustness in environments affected by occlusion, signal degradation, and dynamic change. The framework is grounded in a systematic literature review that identifies the limitations of single-sensor and fixed-weight fusion approaches in disaster scenarios and motivates the adoption of adaptive fusion strategies. Validation is conducted through a structured user-centered evaluation, employing standardized questionnaires to assess usability, cognitive workload, perceived accuracy, trust, and operational suitability for first responders. The results indicate strong perceived utility, reduced cognitive load, and high confidence in AR-assisted decision-making, supporting the potential of adaptive sensor-fusion-based mobile AR as a viable and scalable decision-support tool for civil protection and disaster response.
ER  -