Traditional weather observation methods have limitations in detecting low-altitude,small-scale areas and sudden weather events.They often have insufficient coverage,slow response,or high costs.Multi-rotor unmanned aer...Traditional weather observation methods have limitations in detecting low-altitude,small-scale areas and sudden weather events.They often have insufficient coverage,slow response,or high costs.Multi-rotor unmanned aerial vehicles(UAVs),with their strong vertical take-off and landing ability,precise hovering,flexible movement,and ability to carry various small sensors,are gradually becoming key tools to fill these gaps and build three-dimensional weather observation networks.They show important value in medium-and small-scale weather monitoring and emergency weather support.This paper reviews the main sensors for multi-rotor weather drones,their operating modes,and key supporting technologies,summarizes the current state of technology,and provides references for future development.展开更多
Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a de...Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.展开更多
基金supported by the High-Level Talent Foundation of Natural Science Research Funding Project for Ordinary Universities in Jiangsu Province(grant number.25KJD520004)Jinling Institute of Technology(grant number.JIT-B-202413).
文摘Traditional weather observation methods have limitations in detecting low-altitude,small-scale areas and sudden weather events.They often have insufficient coverage,slow response,or high costs.Multi-rotor unmanned aerial vehicles(UAVs),with their strong vertical take-off and landing ability,precise hovering,flexible movement,and ability to carry various small sensors,are gradually becoming key tools to fill these gaps and build three-dimensional weather observation networks.They show important value in medium-and small-scale weather monitoring and emergency weather support.This paper reviews the main sensors for multi-rotor weather drones,their operating modes,and key supporting technologies,summarizes the current state of technology,and provides references for future development.
基金supported by the China Ministry of Industry and Information Technology Foundation and Aeronautical Science Foundation of China(ASFC-201920007002)the National Key Research and Development Plan(2021YFB1600603)the Open Fund of Key Laboratory of Civil Aircraft Airworthiness Technology,Civil Aviation University of China.
文摘Considering the problem that the scattering echo images of airborne Doppler weather radar are often reduced by ground clutters,the accuracy and confidence of meteorology target detection are reduced.In this paper,a deep convolutional neural network(DCNN)is proposed for meteorology target detection and ground clutter suppression with a large collection of airborne weather radar images as network input.For each weather radar image,the corresponding digital elevation model(DEM)image is extracted on basis of the radar antenna scan-ning parameters and plane position,and is further fed to the net-work as a supplement for ground clutter suppression.The fea-tures of actual meteorology targets are learned in each bottle-neck module of the proposed network and convolved into deeper iterations in the forward propagation process.Then the network parameters are updated by the back propagation itera-tion of the training error.Experimental results on the real mea-sured images show that our proposed DCNN outperforms the counterparts in terms of six evaluation factors.Meanwhile,the network outputs are in good agreement with the expected mete-orology detection results(labels).It is demonstrated that the pro-posed network would have a promising meteorology observa-tion application with minimal effort on network variables or parameter changes.
