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- W2998179544 abstract "This paper presents a novel approach for autonomously estimating the state of an evolving wildfire in real-time. Information fusion regarding the fire is employed where the geospatial probabilistic state of the current fire spread is forecast to a future point in time whereupon this prior belief state is updated from the observations of two distinct types of sensing agents: (i) static temperature sensors embedded on the surface and (ii) mobile vision sensors housed on aerial vehicles. This act of information fusion, as well as the guidance methods that direct the mobile vision sensors to useful sensing locations, are derived under the evidential reasoning framework of the Dempster-Shafer theory of probable reasoning. The recursive bi-directional feedback procedure between the mobile vision agents and the forecasting operation is the primary contribution of this work. This procedure is evaluated via simulation on its ability to estimate a growing wildfire in real-time. The results indicate a definite improvement with the inclusion of mobile vision sensors, although a number of enhancements are noted that can strengthen the current path-planning algorithm." @default.
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- W2998179544 date "2020-01-05" @default.
- W2998179544 modified "2023-09-29" @default.
- W2998179544 title "Estimating the Real-time Spread of Wildfires with Vision-Equipped UAVs and Temperature Sensors via Evidential Reasoning" @default.
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- W2998179544 doi "https://doi.org/10.2514/6.2020-1197" @default.
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