低截获概率(low probability of intercept,LPI)雷达信号凭借其卓越的抗截获能力,在现代电子战中得到了广泛应用。但LPI雷达信号的低峰值功率使其极易被加性白高斯噪声(additive white Gaussian noise,AWGN)淹没,导致信噪比(signal-to-n...低截获概率(low probability of intercept,LPI)雷达信号凭借其卓越的抗截获能力,在现代电子战中得到了广泛应用。但LPI雷达信号的低峰值功率使其极易被加性白高斯噪声(additive white Gaussian noise,AWGN)淹没,导致信噪比(signal-to-noise ratio,SNR)较低,给信号的检测和识别带来了极大的挑战。为了从AWGN背景中提取原始LPI雷达信号,本文提出了一种名为LPI-U-Net的深度神经网络(deep neural network,DNN),用于端到端的时域LPI雷达信号增强。该网络由特征提取模块(feature extract module,FEM)、特征聚焦模块(feature focus module,FFM)和信号恢复模块(signal recover module,SRM)组成。首先FEM通过卷积操作提取信号的特征,然后FFM利用卷积和通道间注意力进一步关注对信号增强任务有利的特征,最后SRM利用反卷积操作从特征中重构信号,从而完成LPI雷达信号增强。仿真实验表明LPI-U-Net在低SNR下的LPI雷达信号增强性能优于传统信号处理中典型的降噪方法,验证了其可行性和有效性。展开更多
In this study, we provide a detailed case study of the X-pattern of equatorial ionization anomaly(EIA) observed on the night of September 12, 2021 by the Global-scale Observations of the Limb and Disk(GOLD) mission. U...In this study, we provide a detailed case study of the X-pattern of equatorial ionization anomaly(EIA) observed on the night of September 12, 2021 by the Global-scale Observations of the Limb and Disk(GOLD) mission. Unlike most previous studies about the X-pattern observed under the severely disturbed background ionosphere, this event is observed under geomagnetically quiet and low solar activity conditions. GOLD's continuous observations reveal that the X-pattern intensity evolves with local time, while its center's longitude remains constant. The total electron content(TEC) data derived from the ground-based Global Navigation Satellite System(GNSS) network aligns well with GOLD observations in capturing the formation of the X-pattern, extending coverage to areas beyond GOLD's observational reach. Additionally, the ESA's Swarm mission show that both sides of the X-pattern can coincide with the occurrence of small-scale equatorial plasma bubbles(EPBs). To further analyze the possible drivers of the X-pattern, observations from the Ionospheric Connection Explorer(ICON) satellite were used. It shows that the latitudinal expansion(or width) between the EIA crests in two hemispheres is proportional(or inversely proportional) to the upward(or downward) plasma drift velocity, which suggests that the zonal electric field should have a notable influence on the formation of EIA X-pattern. Further simulations using the SAMI2 model support this mechanism, as the X-pattern of EIA is successfully reproduced by setting the vertical plasma drift to different values at different longitudes.展开更多
The ionosphere is an important component of the near Earth space environment.The three common methods for detecting the ionosphere with high frequency(HF)radio signals are vertical detection,oblique detection,and obli...The ionosphere is an important component of the near Earth space environment.The three common methods for detecting the ionosphere with high frequency(HF)radio signals are vertical detection,oblique detection,and oblique backscatter detection.The ionograms obtained by these detection methods can effectively reflect a large amount of effective information in the ionosphere.The focus of this article is on the oblique backscatter ionogram obtained by oblique backscatter detection.By extracting the leading edge of the oblique backscatter ionogram,effective information in the ionosphere can be inverted.The key issue is how to accurately obtain the leading edge of the oblique backscatter ionogram.In recent years,the application of pattern recognition has become increasingly widespread,and the YOLO model is one of the best fast object detection algorithms in one-stage.Therefore,the core idea of this article is to use the newer YOLOX object detection algorithm in the YOLO family to perform pattern recognition on the F and E_(s) layers echoes in the oblique backscatter ionogram.After image processing,a single-layer oblique backscatter echoes are obtained.It can be found that the leading edge extraction of the oblique backscatter ionogram obtained after pattern recognition and image processing by the YOLOX model is more fitting to the actual oblique backscatter leading edge.展开更多
电离层天气变化正成为目前空间天气预报最重要的内容之一,建立一个可靠的、精确的电离层特征参量现报和预报系统对空间科学研究及军民用无线电信息系统保障均具有重要价值。基于国际GNSS服务组织(International GNSS Service,IGS)的地基...电离层天气变化正成为目前空间天气预报最重要的内容之一,建立一个可靠的、精确的电离层特征参量现报和预报系统对空间科学研究及军民用无线电信息系统保障均具有重要价值。基于国际GNSS服务组织(International GNSS Service,IGS)的地基GNSS和全球电离层无线电观测站(Global Ionospheric Radio Observatory,GIRO)数字测高仪的实时数据,以国际参考电离层(International Reference Ionosphere,IRI)模型为背景模型,采用高斯-马尔可夫-限带卡尔曼滤波同化技术,结合超大规模矩阵稀疏存储与处理方法,在云计算平台上构建完成了近实时全球电离层数据同化和预报系统(near-Real-Time Global Ionospheric Data AssiMilation and forecasting system,RT-GIDAM)。该系统具备了全球电离层TEC和电子密度的近实时(延时约5 min)、较高空间(5°×2.5°)和时间分辨率(15 min)的同化和预报功能,可为空间物理研究及相关无线电系统应用提供数据支撑。展开更多
In this study,ionosonde observations over Fuke(19.5°N,109.1°E),Wuhan(30.5°N,114.4°E),and Mohe(53.5°N,122.3°E)were analyzed to demonstrate the responses of the sporadic E()to the severe at...In this study,ionosonde observations over Fuke(19.5°N,109.1°E),Wuhan(30.5°N,114.4°E),and Mohe(53.5°N,122.3°E)were analyzed to demonstrate the responses of the sporadic E()to the severe atmospheric disturbances caused by the Tonga volcanic eruptions on January 15,2022.The most prominent signature was the disappearance of the layer after~10:00 UT over Wuhan and Fuke,which was attributed to the vertical drift caused by the eruptions.The occurred intermittently after 13:00 UT following the arrival of the tropospheric Lamb wave.To examine the causal mechanism for the intermittence,we also included data of horizontal winds in the mesosphere and lower thermosphere region recorded by the meteor radars at Wuhan and Mohe in this study.The wind disturbances with periods of~20 hours contributed to the formation of the layer in the nighttime on January 15.展开更多
同全球导航卫星系统(global navigation satellite system, GNSS)获取电离层总电子含量(total electron content, TEC)数据相比,传统的电离层垂测、斜测等短波段数据具有特征参数丰富、高度分辨率高、历史数据多等优点。为利用电离层垂...同全球导航卫星系统(global navigation satellite system, GNSS)获取电离层总电子含量(total electron content, TEC)数据相比,传统的电离层垂测、斜测等短波段数据具有特征参数丰富、高度分辨率高、历史数据多等优点。为利用电离层垂测和斜测数据,研究地下核爆引起的电离层扰动。利用2016年1月6日朝鲜地下核试验当天的斜测、垂测数据分析电离层扰动现象。结果表明,本次地下核爆造成的行波电离层扰动为小尺度电离层扰动,传播速度为150.3~158.7 m/s。同时核爆发生后0.5 h在距离爆点421.4 km处,观测到F2层临界频率(critical frequency of the F2layer, foF2)较月中值增加了0.7 MHz,较1月5日、1月7日在协调世界时(coordinated universal time, UTC)2:00 UTC的增加了0.5 MHz,极有可能是地下核爆通过岩石圈-大气圈-电离层圈耦合机制造成电离层电子浓度增加。本文分析结果与其他文献资料非常吻合。由此可见,基于短波段电离层探测方式感知电离层扰动从而实现地下核爆炸事件的监测,是一种有效的核爆电离层效应监测手段,可与其他直接监测手段相印证,提高核爆事件监测能力。展开更多
This paper analyzed GPS data from the Topo-Iberia network spanning almost 12 years(2008-2020).The data quality information for all 26 Topo-Iberia stations is provided for the first time,complementing the Spanish Geolo...This paper analyzed GPS data from the Topo-Iberia network spanning almost 12 years(2008-2020).The data quality information for all 26 Topo-Iberia stations is provided for the first time,complementing the Spanish Geological Survey’s storage work.Data analyses based on quality indicators obtained using TEQC have been carried out.The guidelines and data quality information from the IGS stations have been considered as the quality references,with the stations ALJI,EPCU,and TIOU standing out as the worst stations,while on the contrary,FUEN,PALM,PILA,and TRIA meet the quality requirements to become an IGS station.The relationship between the GPS data quality and their GAMIT-and Gipsy X-derived postfit ionosphere-free phase residuals has also been investigated,and the results reveal an inversely proportional relationship.It has been found that the stations showing an increase in elevation of the horizon line,also show an increase in cycle slips and multipath,are among the poorest quality stations,and among those with the highest postfit RMS of phase residuals.Moreover,the evolution of the vegetation around the antenna should be considered as it could cause a progressive loss of quality,which is not complying with the IGS standards.The quality assessment shows that the Topo-Iberia stations are appropriate for geodetic purposes,but permanent monitoring would be necessary to avoid the least possible loss of data and quality.In addition,a method to characterize the GNSS data quality is proposed.展开更多
【目的】利用子午工程甚高频(very high frequency,VHF)相干散射雷达观测数据,基于深度学习技术对低纬F区电离层3m尺度不规则体进行识别与特征提取。【方法】本文基于CSPDarknet神经网络技术构建了电离层不规则体事件智能识别模型,并基...【目的】利用子午工程甚高频(very high frequency,VHF)相干散射雷达观测数据,基于深度学习技术对低纬F区电离层3m尺度不规则体进行识别与特征提取。【方法】本文基于CSPDarknet神经网络技术构建了电离层不规则体事件智能识别模型,并基于预训练好的CSPDarknet为骨干网络以及Yolo目标检测算法,构建了电离层不规则体事件定位模型。【结果】所构建不规则体识别模型能自动从整天甚高频相关散射雷达观测数据当中挑选出低纬电离层不规则体,并根据不规则体定位模型提取出不规则体的高度和持续时间。实验结果表明,不规则体识别模型的F1得分达到了85.89%,比EfficientNet模型的F1得分高5.68%;不规则体定位模型的平均精度指标mAP可以达到87.22%,比Yolov5s模型的mAP高4.32%。【局限】模型训练过程中主要利用了海南富克站单台站的观测数据,为提升模型的泛化性能需进一步引入更多台站观测数据。【结论】本文基于深度学习技术首次提出了一套电离层不规则体事件的智能识别与定位方案,极大改善了传统基于阈值法识别不规则体效率低下且依赖专家的问题,提升了电离层不规则体的研究效率。展开更多
基金supported by the National Key R&D Program of China (Grant No. 2022YFF0503700)the special funds of Hubei Luojia Laboratory (220100011)+1 种基金Chao Xiong is supported by the ISSI-BJ project, “the electromagnetic data validation and scientific application research based on CSES satellite”ISSI/ISSI-BJ project “Multi-Scale Magnetosphere–Ionosphere–Thermosphere Interaction”。
文摘In this study, we provide a detailed case study of the X-pattern of equatorial ionization anomaly(EIA) observed on the night of September 12, 2021 by the Global-scale Observations of the Limb and Disk(GOLD) mission. Unlike most previous studies about the X-pattern observed under the severely disturbed background ionosphere, this event is observed under geomagnetically quiet and low solar activity conditions. GOLD's continuous observations reveal that the X-pattern intensity evolves with local time, while its center's longitude remains constant. The total electron content(TEC) data derived from the ground-based Global Navigation Satellite System(GNSS) network aligns well with GOLD observations in capturing the formation of the X-pattern, extending coverage to areas beyond GOLD's observational reach. Additionally, the ESA's Swarm mission show that both sides of the X-pattern can coincide with the occurrence of small-scale equatorial plasma bubbles(EPBs). To further analyze the possible drivers of the X-pattern, observations from the Ionospheric Connection Explorer(ICON) satellite were used. It shows that the latitudinal expansion(or width) between the EIA crests in two hemispheres is proportional(or inversely proportional) to the upward(or downward) plasma drift velocity, which suggests that the zonal electric field should have a notable influence on the formation of EIA X-pattern. Further simulations using the SAMI2 model support this mechanism, as the X-pattern of EIA is successfully reproduced by setting the vertical plasma drift to different values at different longitudes.
基金Supported by the National Natural Science Foundation of China(42104151,42074184,42188101,41727804)。
文摘The ionosphere is an important component of the near Earth space environment.The three common methods for detecting the ionosphere with high frequency(HF)radio signals are vertical detection,oblique detection,and oblique backscatter detection.The ionograms obtained by these detection methods can effectively reflect a large amount of effective information in the ionosphere.The focus of this article is on the oblique backscatter ionogram obtained by oblique backscatter detection.By extracting the leading edge of the oblique backscatter ionogram,effective information in the ionosphere can be inverted.The key issue is how to accurately obtain the leading edge of the oblique backscatter ionogram.In recent years,the application of pattern recognition has become increasingly widespread,and the YOLO model is one of the best fast object detection algorithms in one-stage.Therefore,the core idea of this article is to use the newer YOLOX object detection algorithm in the YOLO family to perform pattern recognition on the F and E_(s) layers echoes in the oblique backscatter ionogram.After image processing,a single-layer oblique backscatter echoes are obtained.It can be found that the leading edge extraction of the oblique backscatter ionogram obtained after pattern recognition and image processing by the YOLOX model is more fitting to the actual oblique backscatter leading edge.
文摘电离层天气变化正成为目前空间天气预报最重要的内容之一,建立一个可靠的、精确的电离层特征参量现报和预报系统对空间科学研究及军民用无线电信息系统保障均具有重要价值。基于国际GNSS服务组织(International GNSS Service,IGS)的地基GNSS和全球电离层无线电观测站(Global Ionospheric Radio Observatory,GIRO)数字测高仪的实时数据,以国际参考电离层(International Reference Ionosphere,IRI)模型为背景模型,采用高斯-马尔可夫-限带卡尔曼滤波同化技术,结合超大规模矩阵稀疏存储与处理方法,在云计算平台上构建完成了近实时全球电离层数据同化和预报系统(near-Real-Time Global Ionospheric Data AssiMilation and forecasting system,RT-GIDAM)。该系统具备了全球电离层TEC和电子密度的近实时(延时约5 min)、较高空间(5°×2.5°)和时间分辨率(15 min)的同化和预报功能,可为空间物理研究及相关无线电系统应用提供数据支撑。
基金the Funds of the National Natural Science Foundation of China(NSFC),grant numbers 42174211,42230207,and U2039205.
文摘In this study,ionosonde observations over Fuke(19.5°N,109.1°E),Wuhan(30.5°N,114.4°E),and Mohe(53.5°N,122.3°E)were analyzed to demonstrate the responses of the sporadic E()to the severe atmospheric disturbances caused by the Tonga volcanic eruptions on January 15,2022.The most prominent signature was the disappearance of the layer after~10:00 UT over Wuhan and Fuke,which was attributed to the vertical drift caused by the eruptions.The occurred intermittently after 13:00 UT following the arrival of the tropospheric Lamb wave.To examine the causal mechanism for the intermittence,we also included data of horizontal winds in the mesosphere and lower thermosphere region recorded by the meteor radars at Wuhan and Mohe in this study.The wind disturbances with periods of~20 hours contributed to the formation of the layer in the nighttime on January 15.
文摘同全球导航卫星系统(global navigation satellite system, GNSS)获取电离层总电子含量(total electron content, TEC)数据相比,传统的电离层垂测、斜测等短波段数据具有特征参数丰富、高度分辨率高、历史数据多等优点。为利用电离层垂测和斜测数据,研究地下核爆引起的电离层扰动。利用2016年1月6日朝鲜地下核试验当天的斜测、垂测数据分析电离层扰动现象。结果表明,本次地下核爆造成的行波电离层扰动为小尺度电离层扰动,传播速度为150.3~158.7 m/s。同时核爆发生后0.5 h在距离爆点421.4 km处,观测到F2层临界频率(critical frequency of the F2layer, foF2)较月中值增加了0.7 MHz,较1月5日、1月7日在协调世界时(coordinated universal time, UTC)2:00 UTC的增加了0.5 MHz,极有可能是地下核爆通过岩石圈-大气圈-电离层圈耦合机制造成电离层电子浓度增加。本文分析结果与其他文献资料非常吻合。由此可见,基于短波段电离层探测方式感知电离层扰动从而实现地下核爆炸事件的监测,是一种有效的核爆电离层效应监测手段,可与其他直接监测手段相印证,提高核爆事件监测能力。
基金supported in part by the University of Jaén and the Spanish Ministry of Economy, Industry and Competitiveness (PTA2015-11507-I MINECO)。
文摘This paper analyzed GPS data from the Topo-Iberia network spanning almost 12 years(2008-2020).The data quality information for all 26 Topo-Iberia stations is provided for the first time,complementing the Spanish Geological Survey’s storage work.Data analyses based on quality indicators obtained using TEQC have been carried out.The guidelines and data quality information from the IGS stations have been considered as the quality references,with the stations ALJI,EPCU,and TIOU standing out as the worst stations,while on the contrary,FUEN,PALM,PILA,and TRIA meet the quality requirements to become an IGS station.The relationship between the GPS data quality and their GAMIT-and Gipsy X-derived postfit ionosphere-free phase residuals has also been investigated,and the results reveal an inversely proportional relationship.It has been found that the stations showing an increase in elevation of the horizon line,also show an increase in cycle slips and multipath,are among the poorest quality stations,and among those with the highest postfit RMS of phase residuals.Moreover,the evolution of the vegetation around the antenna should be considered as it could cause a progressive loss of quality,which is not complying with the IGS standards.The quality assessment shows that the Topo-Iberia stations are appropriate for geodetic purposes,but permanent monitoring would be necessary to avoid the least possible loss of data and quality.In addition,a method to characterize the GNSS data quality is proposed.
文摘【目的】利用子午工程甚高频(very high frequency,VHF)相干散射雷达观测数据,基于深度学习技术对低纬F区电离层3m尺度不规则体进行识别与特征提取。【方法】本文基于CSPDarknet神经网络技术构建了电离层不规则体事件智能识别模型,并基于预训练好的CSPDarknet为骨干网络以及Yolo目标检测算法,构建了电离层不规则体事件定位模型。【结果】所构建不规则体识别模型能自动从整天甚高频相关散射雷达观测数据当中挑选出低纬电离层不规则体,并根据不规则体定位模型提取出不规则体的高度和持续时间。实验结果表明,不规则体识别模型的F1得分达到了85.89%,比EfficientNet模型的F1得分高5.68%;不规则体定位模型的平均精度指标mAP可以达到87.22%,比Yolov5s模型的mAP高4.32%。【局限】模型训练过程中主要利用了海南富克站单台站的观测数据,为提升模型的泛化性能需进一步引入更多台站观测数据。【结论】本文基于深度学习技术首次提出了一套电离层不规则体事件的智能识别与定位方案,极大改善了传统基于阈值法识别不规则体效率低下且依赖专家的问题,提升了电离层不规则体的研究效率。
