空时自适应滤波抗干扰分析

GPS抗干扰性能差的缺点日益突出,空时自适应滤波是GPS接收机抗干扰技术上的重大突破.通过对不同情况下的空时自适应滤波权值空频响应的数字仿真和分析,得出空时自适应滤波抗干扰的一些有益结论,并给出合理解释.     

一种卫星移动多媒体广播COFDM系统的可用性研究

旨在研究COFDM系统应用在卫星移动多媒体广播中的可用性,文章给出一种基于“级联编码 相干检测 PN序列信道估计”方案的COFDM系统的基带模型,并对其进行了建模与仿真,具体内容包括收发端主要模块的系统构架设计以及信道干扰模型的设计与分析,然后给出基于本仿真所得到的系统误码率在卫星移动多径衰落传输环境中的变化情况,并且分析了仿真结果,最后验证了这种COFDM系统在卫星移动多媒体广播领域中的可用性。     

基于EEG的驾驶舱告警信息设计研究

针对直升机驾驶舱人机交互中认知负荷问题，设计飞行模拟试验，探究视觉和听觉告警对飞行员认 知负荷的影响。基于多元线性回归方法，通过主观评价结果、反应时间、准确率和脑电信号等指标，构建了直 升机驾驶舱人机交互认知负荷评估模型。结果表明文字颜色、语音提示两种告警特征对飞行员信息加工均会产 生影响，使用多元线形回归模型可对不同告警方式下受试对象的认知负荷水平进行评估，构建的评估模型能够 较准确地评估直升机驾驶舱中飞行员的认知负荷，可为优化驾驶舱交互设计、降低飞行员认知负荷、减少人为 差错提供依据。     

Blind identification of space–time block codes based on deep learning

Deep Learning(DL) has important applications to both commercial and military communications, such as software-defined radio, cognitive radio and spectrum surveillance. While DL has been intensively studied for modulation recognition, there are very few investigations for blind identification of Space-Time Block Codes(STBCs). This paper proposes a Residual Network(RN)-based model for identifying 6 kinds of STBC signals with a single receiving antenna, including the same length of coding matrix. I...     

齿轮故障识别的密度峰值聚类欠定盲源分离算法

为提高盲源分离算法在振动源数目估计问题中的噪声鲁棒性，提出了一种基于密度峰值聚类的欠定盲源分离算法。对预处理后的信号提取单源点，通过密度峰值聚类对单源点进行聚类得到混合矩阵的估计值。通过基于压缩感知模型对源信号进行重构，得到分离信号。为验证所提算法分离准确性和噪声鲁棒性，用所提算法对不同信噪比下的仿真信号进行分离，结果显示：在信噪比不低于4 dB时，所提算法均可以准确分离出源信号，算法的准确性和鲁棒性得到验证。设计旋转部件故障诊断试验台对所提算法在实际应用中的有效性进行验证，对实测复合故障振动信号进行分离，试验结果表明该算法成功分离出观测信号中的锥齿轮和行星齿轮单一故障特征，有助于工程中旋转部件故障诊断。     

采用局部约束线性编码的像素级舰船目标图像融合

为了提升视觉信息在低能见度条件下的适用性,提出了一种采用局部约束线性编码的像素级舰船目标图像融合方法.首先,采用K均值奇异值分解算法从海量的训练样本中完成过完备字典的学习.其次,在考虑着舰导引实时性任务需求的前提下,采用局部约束线性编码完成融合系数的非迭代求解,相较于压缩感知理论架构下的匹配追踪算法,极大地降低了计算复...     

Transfer learning: A new aerodynamic force identification network based on adaptive EMD and soft thresholding in hypersonic wind tunnel

The aerodynamic test in the pulse combustion wind tunnel is very important for the design, evaluation and optimization of aerodynamic characteristics of the hypersonic aircraft. The test accuracy even affects the success or failure of hypersonic aircraft development. In the aerodynamic test of pulse combustion wind tunnel, the aerodynamic signal is disturbed by the inertial force signal, which seriously affects the test accuracy of aerodynamic force. Aiming at the above problems, this paper innovatively proposes an aerodynamic intelligent identification method, that is the transfer learning network based on adaptive Empirical Modal Decomposition (EMD) and Soft Thresholding (TLN-AE&ST). Compared with the existing aerodynamic intelligent identification model based on deep learning technology, this study introduces the transfer learning idea into the aerodynamic intelligent identification model for the first time. The TLN-AE&ST effectively alleviates the problem of scarcity of training samples for intelligent models due to the high cost of wind tunnel tests, and provides a new idea for further implementation of deep learning technology in the field of wind tunnel aerodynamic testing. And this study designed residual attention block with soft threshold and dense block with adaptive EMD in TLN-AE&ST model. Residual attention block with soft threshold module can more effectively suppress the influence of instrument noise signal on model training effect. Dense block with adaptive EMD makes the deep learning model no longer a black box to a certain extent, and has certain physical significance. Finally, a series of wind tunnel tests were carried out in the Φ = 2.4 m pulse combustion wind tunnel of China Aerodynamic Research and Development Center to verify the effectiveness of TLN-AE&ST.