一种正交跳频信号动态分选方法

针对复杂电磁环境下正交跳频信号分选时效不高问题,提出了一种正交跳频信号动态分选方法。首先基于滑动窗口的数据流模型,采用构造型神经网络对跳频信号频域数据和方位信息动态聚类,减轻噪声等因素影响,解决方位信息和幅度关联模糊性的问题;再在相应的覆盖簇内运用时频关联方法,实现正交跳频信号的动态分选,实验结果表明该方法是有效的。
     

基于恒模特性的QPSK-DSSS信号扩频序列估计

 针对非合作低信噪比条件下的QPSK-DSSS信号,提出一种基于恒模特性的扩频序列估计算法。该算法首先将单用户QPSK-DSSS信号等效为两用户的BPSK-DSSS信号,其次通过对信号协方差矩阵进行特征分解,估计出由同相与正交两路扩频序列张成的二维信号子空间,最后利用扩频序列的恒模特性消除特征分解带来的酉阵模糊问题,实现扩频序列的精确估计。本文提出算法实现简单,相对目前现有传统算法具有较低的计算复杂度,而且计算机仿真表明:本文提出算法在低信噪比条件下具有优良的估计性能。     

从天基战略侦察向天基战术侦察的发展

在简述天基情报侦察发展历史的基础上,较为详细地分析了战术天基情报侦察的基本要求,并在此基础上给出了战术天基情报侦察的体系结构。最后列举了美国的"战术卫星-3"系统,用以说明战术天基情报侦察的发展趋势。     

基于双驱调制的高速光电探测器频响自校准表征

针对当前高速光电探测器频率响应测试的问题,提出了基于双驱调制的高速光电探测器频响自校准表征方法。该方法通过设置双驱调制频率为近似相等关系克服了电-光响应器件不平坦响应的额外校准,实现了光电探测器频率响应的自校准表征。同时,通过采用双驱调制频率的和频与差频拍频边带的相对功率来获得光电探测器的频率响应,摆脱了双驱马赫-曾德尔调制器偏置漂移的影响并扩展了两倍以上的测试带宽。相比于全光激励法,提高了测量精度与动态范围;与传统电光激励法相比,克服了强度调制器偏置漂移的影响和额外校准的难题;优于先前的双音调制法,简化了测试系统和降低了测量成本。光电探测器频率响应的实验测量结果与强度噪声法测量结果以及与该光电探测器出厂数据的一致性验证了该方法的正确性。     

Few-shot electromagnetic signal classification: A data union augmentation method

Deep learning has been fully verified and accepted in the field of electromagnetic signal classification. However, in many specific scenarios, such as radio resource management for aircraft communications, labeled data are difficult to obtain, which makes the best deep learning methods at present seem almost powerless, because these methods need a large amount of labeled data for training. When the training dataset is small, it is highly possible to fall into overfitting, which causes performance degradation of the deep neural network. For few-shot electromagnetic signal classification, data augmentation is one of the most intuitive countermeasures. In this work, a generative adversarial network based on the data augmentation method is proposed to achieve better classification performance for electromagnetic signals. Based on the similarity principle, a screening mechanism is established to obtain high-quality generated signals. Then, a data union augmentation algorithm is designed by introducing spatiotemporally flipped shapes of the signal. To verify the effectiveness of the proposed data augmentation algorithm, experiments are conducted on the RADIOML 2016.04C dataset and real-world ACARS dataset. The experimental results show that the proposed method significantly improves the performance of few-shot electromagnetic signal classification.