利用无线通信链路进行基于深度学习的大雾天气监测

为了以低成本、高时空分辨率进行大雾天气监测，提出一种利用无线通信链路进行基于深度学习的大雾天气监测方法。由于信道中不同浓度的大雾天气在信号中留有的特征不同，采集了4种不同浓度大雾下的无线电信号，建立无线电大雾天气监测数据集；通过在传统ResNet50网络中引入注意力机制并进行特征融合，得到改进后的A-ResNet50模型。利用A-ResNet50网络提取接收信号中留有的不同浓度大雾天气的特征，对四类不同浓度大雾天气进行分类识别，达到监测大雾天气的目的。所提方法在建立的数据集上进行了验证，相较于其他传统分类算法，本方法性能最优，最终识别准确率达到86.18 %，结果证明了该方法的可行性和有效性。

Deep Learning-based Foggy Weather Monitoring via Wireless Communication Link

In order to monitor foggy weather at low cost and high temporal and spatial resolution,a deep learning-based foggy weather monitoring via wireless communication method was proposed in this paper.Since different concentrations of foggy weather in the channel leave different features in the signal,this paper collects radio signals under four different concentrations of foggy weather to establish the foggy weather monitoring dataset.By introducing an attention mechanism in the conventional ResNet50 network and performing feature fusion,an improved A-ResNet50 model is obtained.The A-ResNet50 network is used to extract the features of different concentrations of foggy weather left in the received signals,and to classify and identify four types of different concentrations of foggy weather for the purpose of monitoring foggy weather.The proposed method was validated on the dataset established in this paper,and compared with other traditional classification algorithms,the network model proposed in this paper has the best performance.The final recognition accuracy reached 86.18 %, and the result proved the feasibility and effectiveness of the method.