An introduction of mountain-based GPS radio occultation experiments in China

The mountain-based GPS radio occultation is a novel approach to lower atmospheric profiling. The experiments of the mountain-based GPS radio occultation were conducted on the top of Mt. Yaogu (29.38°N, 113.68°E, ∼1240 m) on December 17, 2003, and on the top of Mt. Jiugong (29.39°N, 114.65°E, ∼1550 m) on July 24, 2004. Based on these observation data, the scientific data processing software has been developed and is used to retrieve successfully the atmospheric refractivity profiles. The validation experiment was performed on the top of Mt. Wuling (40.60°N, 117.48°E, ∼2118 m) during August 1–29, 2005. Collocated automatic weather station and the radiosondes nearby were operated simultaneously for the comparison campaign. Results show that the radio occultation technique obtained about 40 profiles every day with the receiver antenna pointing to the south. Comparisons show that the refractivity measured by occultation agree well with those by the radiosondes, but not well with those by the automatic weather station due to their much different geographic locations of measurements. Results of these experiments suggest that the mountain-based GPS radio occultation is an economic reliable novel technique monitoring temporal and spatial variations of local lower atmospheric environments.     

Analysis of solar spike events by means of symbolic dynamics methods

We have searched for interrelations of spikes emitted simultaneously at different frequencies during the impulsive phase of flare events (Fig.1). As the spikes are related to the flare energy release and are interpreted as emissions that originate at different sites having different magnetic field strengths, any relation in frequency is interpretated as a relation in space. Quantities of symbolic dynamics, such as mutual information, Shannon information and algorithmic complexity are appropriate to characterize such spatiotemporal patterns, whereas the popular estimate of fractal dimensions can be applied to low-dimensional systems only.The goal is to decide between two possible types of fragmentation depending on the energy release and emission processes, which we callglobal andlocal organization. In the global organization the whole region becomes supercritical, and the energy is released in independent, small regions. The alternative local scenario requires a trigger that spreads from initial localized events and ignites nearby regions.Mutual information which is a generalization of correlation indicates a relation in frequency beyond the bandwidth of individual spikes. The scans in the spectrograms with large mutual information also show a low level of Shannon information and algorithmic complexity, indicating that the simultaneous appearance of spikes at other frequencies is not a completely stochastic phenomenon (white noise). It may be caused by a nonlinear deterministic system or by a Markov process. By means of mutual information we find a memory over frequency intervals up to 60 MHz (Fig. 2). Shannon information and algorithmic complexity, however, describe spike events as a whole, i.e. a global source region. A global organization is also apparent in quasi-periodic changes of the Shannon information and algorithmic complexity in the range of 2–8 seconds (Fig. 3).This findings is compatible with a scenario of local organization in which the information of one spike event spreads spatially and hence triggers further spike events at different places. The region is not an ensemble of independently flashing sources, each representing a system that cascades in energy after an initial trigger. On the contrary, there is a causal connection between the sources at any time.The analysis of four spike events suggests that the simultaneous appearance of spikes is not stochastically independent but a process in which spikes at nearby locations are simultaneously triggered by a common exciter.We have shown in the case in the case of spikes that quantities from nonlinear dynamics used in this paper are helpful in detecting structural properties of complex spatio-temporal patterns. This approach seems to be promising also for several other astrophysical applications.     

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.