Unintentional modulation evaluation in time domain and frequency domain

With the development of wireless communication technology, the electromagnetic environment has become more and more complex. Conventional signal identification methods are difficult to accurately identify illegal devices. However, electromagnetic signals have an unavoidable device-specific characteristic unintentionally generated by a transmitter, appearing in the form of an Un Intentional Modulation(UIM), namely Radio Frequency Fingerprint(RFF). RFFs can be used to uniquely identify an emitter ...     

BDS-3多种授时方法精度试验及比较分析

北斗卫星导航系统(BDS)本质上是一个高精度时间空间信息服务系统,是我国 自主运行的重要空间基础设施.BDS-3已于2020年7月正式开通,向广大用户提供RNSS、SBAS、RDSS单向和RDSS双向等多种授时服务.针对BDS-3提供的各种授时服务进行了简要介绍,详细讨论了各种授时方法,并利用实测数据进行了试验验证和比较分析.结果表明,BDS-3授时服务精度全部优于公布的指标要求,其中精度最高的SBAS授时方法精度可达2ns左右,RDSS双向授时精度和RNSS授时精度相当,达到9ns左右,RDSS单向授时精度最差,在15～30ns左右.     

Large-scale real-world radio signal recognition with deep learning

In the past ten years, many high-quality datasets have been released to support the rapid development of deep learning in the fields of computer vision, voice, and natural language processing. Nowadays, deep learning has become a key research component of the Sixth-Generation wireless systems (6G) with numerous regulatory and defense applications. In order to facilitate the application of deep learning in radio signal recognition, in this work, a large-scale real-world radio signal dataset is created based on a special aeronautical monitoring system - Automatic Dependent Surveillance-Broadcast (ADS-B). This paper makes two main contributions. First, an automatic data collection and labeling system is designed to capture over-the-air ADS-B signals in the open and real-world scenario without human participation. Through data cleaning and sorting, a high-quality dataset of ADS-B signals is created for radio signal recognition. Second, we conduct an in-depth study on the performance of deep learning models using the new dataset, as well as comparison with a recognition benchmark using machine learning and deep learning methods. Finally, we conclude this paper with a discussion of open problems in this area.     

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。     

Analysis of NASA’s DSN Venus Express radio occultation data for year 2014

The Venus Express Radio Science Experiment (VeRa) was part of the scientific payload of the Venus Express (VEX) spacecraft and was targeted at the investigation of Venus’ atmosphere, surface, and gravity field as well as the interplanetary medium. This paper describes the methods and the required calibrations applied to VEX-VeRa raw radio occultation data used to retrieve vertical profiles of Venus’ ionosphere and neutral atmosphere. In this work we perform an independent analysis of a set of 25 VEX, single-frequency (X-band), occultations carried out in 2014, recorded in open-loop at the NASA Deep Space Network. Our temperature, pressure and electron density vertical profiles are in agreement with previous studies available in the literature. Furthermore, our analysis shows that Venus’ ionosphere is more influenced by the day/night condition than the latitude variations, while the neutral atmosphere experiences the opposite. Our scientific interpretation of these results is based on two major responsible effects: Venus’ high thermal inertia and the zonal winds. Their presence within Venus’ neutral atmosphere determine why in these regions a latitude dependence is predominant on the day/night condition. On the contrary, at higher altitudes the two aforementioned effects are less important or null, and Venus’ ionosphere shows higher electron density peaks in the probed day-time occultations, regardless of the latitude.