With the growing complexity of electromagnetic environments,electromagnetic interference(EMI)poses a significant threat to the quality of weather radar data.However,identifying EMI echoes presents substantial challeng...With the growing complexity of electromagnetic environments,electromagnetic interference(EMI)poses a significant threat to the quality of weather radar data.However,identifying EMI echoes presents substantial challenges due to their small target sizes and diverse morphological characteristics,frequently leading to misclassification and missed detections in practical application.To address these limitations,a deep learning-based EMI echo identification algorithm is proposed in this study.The algorithm enhances You Only Look Once(YOLO)object detection framework through several innovations:integrating Switchable Atrous Convolution(SAConv)into the C2f module of the backbone network to improve multi-scale feature representation,combining the Large Separable Kernel Attention(LSKA)mechanism with the Spatial Pyramid Pooling Fast(SPPF)module to refine feature extraction,incorporating the Convolutional Block Attention Module(CBAM)into the segmentation head to emphasize critical features while suppressing irrelevant interference,and employing dynamic Wise Intersection over Union(Wise-IoU)v2 as the bounding box regression loss to improve localization accuracy.With these architectural improvements,a novel YOLO Radar Electromagnetic Interference Echo(YOLO-REIE)model is developed and trained on a dataset of EMI echo samples from the Suizhou S-band weather radar.Comprehensive evaluation demonstrates that YOLO-REIE achieves superior performance,with a precision of 95.76%,a recall of 95.31%,and a mean Average Precision at an IoU threshold 0.5(mAP50)of 93.32%,significantly outperforming other object detection models.The research findings provide an efficient technical approach for the identification of EMI echoes,thereby guaranteeing the quality and reliability of weather radar data.展开更多
Resonant beam communications (RBCom), which adopts oscillating photons between two separate retroreflectors for information transmission, exhibits potential advantages over other types of wireless optical communicatio...Resonant beam communications (RBCom), which adopts oscillating photons between two separate retroreflectors for information transmission, exhibits potential advantages over other types of wireless optical communications (WOC). However, echo interference generated by the modulated beam reflected from the receiver affects the transmission of the desired information. To tackle this challenge, a synchronization-based point-to-point RBCom system is proposed to eliminate the echo interference, and the design for the transmitter and receiver is discussed. Subsequently,the performance of the proposed RBCom is evaluated and compared with that of visible light communications(VLC)and free space optical communications (FOC). Finally, future research directions are outlined and several implementation challenges of RBCom systems are highlighted.展开更多
基金Supported by the National Natural Science Foundation of China(42005121 and 42575172)Joint Fund of Hubei Province Natural Science Foundation(2023AFD095)+3 种基金Open Fund of Heavy Rainfall Research of China(BYKJ2024Z08)Key Laboratory of South China Sea Meteorological Disaster Prevention and Mitigation of Hainan Province(SCSF202203)Open Grants of China Meteorological Administration Radar Meteorology Key Laboratory(2023LRM-B07)Joint Project of Wuhan Meteorological Science and Technology(2024020901030452)。
文摘With the growing complexity of electromagnetic environments,electromagnetic interference(EMI)poses a significant threat to the quality of weather radar data.However,identifying EMI echoes presents substantial challenges due to their small target sizes and diverse morphological characteristics,frequently leading to misclassification and missed detections in practical application.To address these limitations,a deep learning-based EMI echo identification algorithm is proposed in this study.The algorithm enhances You Only Look Once(YOLO)object detection framework through several innovations:integrating Switchable Atrous Convolution(SAConv)into the C2f module of the backbone network to improve multi-scale feature representation,combining the Large Separable Kernel Attention(LSKA)mechanism with the Spatial Pyramid Pooling Fast(SPPF)module to refine feature extraction,incorporating the Convolutional Block Attention Module(CBAM)into the segmentation head to emphasize critical features while suppressing irrelevant interference,and employing dynamic Wise Intersection over Union(Wise-IoU)v2 as the bounding box regression loss to improve localization accuracy.With these architectural improvements,a novel YOLO Radar Electromagnetic Interference Echo(YOLO-REIE)model is developed and trained on a dataset of EMI echo samples from the Suizhou S-band weather radar.Comprehensive evaluation demonstrates that YOLO-REIE achieves superior performance,with a precision of 95.76%,a recall of 95.31%,and a mean Average Precision at an IoU threshold 0.5(mAP50)of 93.32%,significantly outperforming other object detection models.The research findings provide an efficient technical approach for the identification of EMI echoes,thereby guaranteeing the quality and reliability of weather radar data.
基金supported in part by the Natural Science Foundation of China under Grant 62341112in part by the Basic Research Project of Hetao Shenzhen-HK S&T Cooperation Zone under Grant HZQBKCZYZ-2021067+3 种基金in part by the Key Project of Shenzhen under Grant JCYJ20220818103006013in part by Shenzhen High-Tech Zone Project under Grant KC2022KCCX0041in part by Guangdong Provincial Key Laboratory of Future Networks of Intelligence under Grant 2022B1212010001in part by Shenzhen Key Laboratory of Big Data and Artificial Intelligence under Grant ZDSYS201707251409055.
文摘Resonant beam communications (RBCom), which adopts oscillating photons between two separate retroreflectors for information transmission, exhibits potential advantages over other types of wireless optical communications (WOC). However, echo interference generated by the modulated beam reflected from the receiver affects the transmission of the desired information. To tackle this challenge, a synchronization-based point-to-point RBCom system is proposed to eliminate the echo interference, and the design for the transmitter and receiver is discussed. Subsequently,the performance of the proposed RBCom is evaluated and compared with that of visible light communications(VLC)and free space optical communications (FOC). Finally, future research directions are outlined and several implementation challenges of RBCom systems are highlighted.
