机载捷联航姿系统精度超差故障分析

捷联航向姿态参考系统是一种重要的机载导航设备,可实现飞机航向、姿态、角速率,以及加速度多维信息的测量。针对某型捷联航姿系统在改装试飞过程中出现的精度超差故障,结合工作原理和捷联航姿系统软硬件设计方案,进行故障机理分析,建立故障树。利用故障树,对底事件进行逐一排查,筛查问题原因,成功定位并顺利排除该故障。通过故障树的定性分析表明,精度超差的原因是系统软件设计中将轮载信号作为地速反馈的先决条件所致。     

非参数化迭代自适应SAR射频干扰抑制方法

合成孔径雷达（Synthetic Aperture Radar，SAR）的成像质量与图像解译精度会因为空间中存在的射频干扰（Radio Frequency Interference，RFI）而下降。本文在加权最小二乘迭代自适应算法的基础上，提出了非参数化的迭代自适应RFI抑制方法。该方法在成像处理前增加了对RFI的预检测环节，在初步检测出存在RFI的数据部分后只对存在RFI的数据进行SG（Savitsky-Golay，萨维茨基·戈莱）卷积平滑处理以提升对弱RFI的检测能力。利用带阻滤波器滤除存在干扰的频段后，使用迭代自适应处理在频谱中估计出被去除频段中的信号分量以获得期望的距离谱。与利用传统距离频率滤波方法进行RFI抑制相比，所提出的方法在图像细节保持方面效果更优，避免了因频谱不连续而导致的旁瓣增加，且运算效率更高。     

基于TestCenter软件平台自动测试系统设计

随着通信技术的快速发展，目前市场上无线电测试类仪器保有量快速扩充，测试任务逐年增长。面对快速增长业务，传统的人工手动测试方式已不能满足市场需求。针对以上问题，采用TestCenter软件平台，结合已有计量标准设备，适当增加接口及智能开关设备，设计了一套通用自动测试系统。经验证，该自动测试系统操作简单，运行可靠，测试速度快。     

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 ...     

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.     

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.