基于小波变换的射频干扰消除方法的研究

在射电天文观测中,射频干扰(Radio Frequency Interference, RFI)会以多种形式混入望远镜接收系统,给观测带来误判或者降低观测信噪比.近年来国内国际射电天文快速发展,国内国际大型射电望远镜和阵列先后建设,观测灵敏度大为提高,射频干扰的影响尤为突出.随着科技发展和人类活动的加剧,射频干扰日益严重且不可逆转.提出利用2维离散小波变换的方法分析射电天文观测的数据,对望远镜系统输出的时间频率序列进行小波变换,根据小波系数分离出原始信号中各分量,每个分量统计得到相应的阈值,将各分量与阈值相比较识别干扰成分并标记去除.利用该方法对实际观测数据进行了处理,结果表明该方法能够很好地标记并消减干扰信号,且提高了观测的信噪比.     

相控阵馈源MVDR波束合成器缓解RFI仿真

相控阵馈源(Phased array feeds, PAFs)接收机作为下一代微波接收机, 为大口径射电天文望远镜的射电干扰(Radio Frequency Interference, RFI)缓解工作带来了新的解决方法. PAFs接收机对射电望远镜焦平面的电磁波进行空域采样, 返回时域阵列信号, 使用最小方差无失真响应(Minimum Variance Distortionless Response, MVDR)波束合成器可以自适应地识别RFI的方向, 同时抑制RFI在输出信号中的功率, 从而达到提升射电望远镜灵敏度的效果. 仿真结果表明MVDR波束合成器对有源高能量的射电干扰有很强的识别能力和一定程度的缓解能力, 同时, 该波束合成器对各阵元信道中加性噪声累积引起的无源干扰有很强的抑制能力, 因此, PAFs接收机的MVDR波束合成器可以增强日益复杂电磁波环境下射电望远镜的抗干扰能力.     

射电望远镜远场区域强干扰源规避方法研究

随着频率使用率的提高, 射电天文台址地面或空间存在强电磁干扰致使望远镜接收机系统处于非线性状态. 为减少强电磁干扰的影响、提高天文观测效率, 提出了一种基于望远镜远场区域的强干扰源规避方法. 首先, 通过仿真分析确定的射电望远镜远场方向图, 结合望远镜与干扰源之间的位置关系, 分析了强电磁干扰到达射电望远镜焦点处的功率响应, 并依据接收机第2阶中频放大器性能参数, 确定射电望远镜处于非饱和状态的规避角度计算方法. 其次, 采用该方法计算分析了民航飞机对射电望远镜的影响, 若民航飞机上有主动发射的干扰源, 且不经过反射等传播现象, 当射电望远镜主波束轴偏开一定方向后, 可有效降低对射电望远镜的干扰强度.     

Detection and Correction of AMSR-E Radio-Frequency Interference

Radio-frequency interference (RFI) affects greatly the quality of the data and retrieval products from space-borne microwave radiometry. Analysis of the Advanced Microwave Scanning Radiometer on the Earth Observing System (AMSR-E) Aqua satellite observations reveals very strong and widespread RFI contam- inations on the C- and X-band data. Fortunately, the strong and moderate RFI signals can be easily identified using an index on observed brightness temperature spectrum. It is the weak RFI that is diffi- cult to be separated from the nature surface emission. In this study, a new algorithm is proposed for RFI detection and correction. The simulated brightness temperature is used as a background signal (B) and a departure of the observation from the background (O-B) is utilized for detection of RFI. It is found that the O-B departure can result from either a natural event (e.g., precipitation or flooding) or an RFI signal. A separation between the nature event and RFI is further realized based on the scattering index (SI). A positive SI index and low brightness temperatures at high frequencies indicate precipitation. In the RFI correction, a relationship between AMSR-E measurements at 10.65 GHz and those at 18.7 or 6.925 GHz is first developed using the AMSR-E training data sets under RFI-free conditions. Contamination of AMSR-E measurements at 10.65 GHz is then predicted from the RFI-free measurements at 18.7 or 6.925 GHz using this relationship. It is shown that AMSR-E measurements with the RFI-correction algorithm have better agreement with simulations in a variety of surface conditions.     

GPS射频干扰浅析

由于GPS接收机的工作有赖于GPS卫星信号，所以易受射频干扰(RFI)的影响。射频干扰可能会引起导航精度的降低或接收机跟踪的丢失。本文论述了信号噪声比和信号噪声密度比的概念，分析了射频干扰对GPS接收的影响，最后说明了直接检测RFI的方法以及RFI源的定位。     

Applications of an AMSR-E RFI Detection and Correction Algorithm in 1-DVAR over Land

Land retrievals using passive microwave radiometers are sensitive to small fluctuations in land brightness temperatures. As such, the radio-frequency interference （RFI） signals emanating from man-made microwave radiation transmitters can result in large errors in land retrievals. RFI in C-and X-band channels can con-taminate remotely sensed measurements, as experienced with the Advanced Microwave Scanning Radiometer （AMSR-E） and the WindSat sensor. In this work, applications of an RFI detection and correction algorithm in retrieving a comprehensive suite of geophysical parameters from AMSR-E measurements using the one-dimensional variational retrieval （1-DVAR） method are described. The results indicate that the values of retrieved parameters, such as land skin temperature （LST）, over these areas contaminated by RFI are much higher than those from the global data assimilation system （GDAS） products. The results also indicate that the differences between new retrievals and GDAS products are decreased evidently through taking into account the RFI correction algorithm. In addition, the convergence metric （χ2） of 1-DVAR is found to be a new method for identifying regions where land retrievals are affected by RFI. For example, in those regions with much stronger RFI, such as Europe and Japan, χ2 of 1-DVAR is so large that convergence cannot be reached and retrieval results may not be reliable or cannot be obtained. Furthermore,χ2 also decreases with the RFI-corrected algorithm for those regions with moderate or weak RFI. The results of RFI detected byχ2 are almost consistent with those identified by the spectral difference method.     

Scalable framework of intelligent RFI flagging for large-scale HI survey data from FAST

Radio frequency interference (RFI) identification is a key step in radio data processing. In order to efficiently process huge volumes of data produced by modern large radio telescopes, such as the Five-hundred-meter Aperture Spherical radio Telescope (FAST), exceptional balance between accuracy and performance (throughput) is required for RFI flagging algorithms. RFI-Net is a single-process RFI identification package based on deep learning technique, and has achieved a higher flagging accuracy than the classical SumThreshold method. In this paper, we present a scalable RFI flagging toolkit, which can drive parallel workflows on multi-CPU and multi-GPU clusters, with RFI-Net as its core detector. It can automatically schedule the workload and aggregate itself after errors according to the running environment. Moreover, its main components are all pluggable, and can be easily customized according to requirements. The experiments with real data of FAST showed that using eight parallel workflows, the toolkit can process sky survey data at a speed of 66.79 GB/h, which means quasi-real-time RFI flagging can be achieved considering the data rate of FAST extragalactic spectral line observations.     

射频干扰检测的SumThreshold算法

射频干扰(Radio Frequency Interference, RFI)是射电目标搜寻和精确分析研究的关键影响因素,因此RFI检测是相关数据处理中的一个重要环节.已有RFI检测方法可分为成分分解法、阈值分析法、机器学习类方法.从应用广泛性和可解释性方面考虑,阈值分析法最具代表性,特别是SumThreshold是近年备受关注的一个RFI检测方法.从SumThreshold方法的原理、算法设计、优化要点、适用性等方面进行介绍和探讨,以供同行参考.     

大面积水体上空星载微波辐射计的干扰识别

卫星微波仪器接收的来自地气系统的被动热辐射与主动传感器发射的信号相混合,被称为无线电频率干扰 (RFI),在主动及被动微波遥感探测领域已成为越来越严重的问题。海洋表面反射的静止通讯、电视卫星下发信号是干扰海洋上星载被动微波辐射计观测的主要来源。该文以先进的微波扫描辐射计AMSR-E为例,采用双主成分分析方法对美国陆地上大面积水体、附近洋面和中国海岸线附近的RFI进行识别,研究表明:美国附近洋面区域星载微波辐射计18.7 GHz通道观测主要受静止电视卫星DirecTV的干扰,由于海表反射引起的RFI非常依赖于静止卫星和星载被动仪器的相对几何位置,只有当闪烁角θ(观测视场镜面反射的静止电视卫星信号方向与视场到星载仪器方向之间的夹角) 较小时卫星观测易受到污染。美国海洋区域较强RFI分布在五大湖区域,离内陆越近RFI越强,东西海岸RFI较强,而整个南海岸干扰相对较弱。中国海岸线附近AMSR-E 6.925 GHz通道观测受RFI影响,而18.7 GHz通道观测未受到干扰。     

Study of Radio Frequency Interference Mitigation Method Based on Wavelet Transform

In radio astronomy observations, radio frequency interference (RFI) is mixed into the telescope receiving system in various forms. The existence of RFI brings misjudgment to the observation or reduces the observational signal-to-noise ratio. In recent years, the domestic and overseas radio astronomy has developed rapidly. Large-scale radio telescopes and telescope arrays at home and abroad have been constructed successively. Observation sensitivity is greatly improved, and the influence of RFI is particularly prominent. With the development of science and technology and the intensification of human activities, the RFI has become increasingly serious and irreversible. We propose to use the two-dimensional discrete wavelet transform method to analyze the data of radio astronomy observations, and to perform wavelet transform on the time-frequency sequence output by the telescope system. According to the wavelet coefficients, the every component in the original signal is separated, and the corresponding threshold value is obtained by the statistics on each component. Each component is compared with the threshold to identify the interference component, and to mark it for removal. This method is used to process the actual observation data. The results show that this method can well mark and eliminate the interference signal, and improve the observational signal-to-noise ratio.