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Wireless Sensor Network Informed UAV Path Planning for Soil Moisture Mapping
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AI
DOI：10.1109/TGRS.2021.3088658.png)
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Abstract
En 中文
Adaptive and targeted allocation of mobile sensing agents, in the form of unmanned aerial vehicles (UAVs) with software defined radar (UAV-SDRadar) payloads, enable mapping of surface soil moisture in regions where in situ wireless sensor networks (WSNs) undersample soil moisture or upscaling models perform poorly. This work presents an optimization-based UAV path planning methodology that seeks to maximize UAV flight coverage over areas where a complementing WSN yields upscaled soil moisture estimates with high uncertainty. By recursively mapping soil moisture over such areas, the combined UAV and WSN instrumentation can gradually capture the domain's true mean soil moisture. A series of numerical simulations are presented to demonstrate the algorithm's basic function while considering real-world and feasible operational scenarios.
Keywords:
Soil moisture
Wireless sensor networks
Path planning
Uncertainty
Moisture
Unmanned aerial vehicles
Soil
Mixed integer programing (MIP)
optimization
software defined radar (SDRadar)
soil moisture
unmanned aerial vehicle (UAV)
wireless sensor networks (WSNs)
Journal
IF:
8.6
Papers:
2.1W
Citations:
10.7W