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...     

一种MEMS陀螺仪零偏建模与估计方法

针对MEMS陀螺仪零偏随时间变化的问题,提出了一种MEMS陀螺仪的零偏建模与估计方法.通过分析静态条件下陀螺仪零偏变化的影响因素,建立了陀螺仪零偏和温度之间的差分方程模型.采用系统辨识的方法,给出了系统模型的参数辨识流程.基于最小二乘法,实现了模型参数的辨识.设计了测试验证试验,基于陀螺仪的零偏样本特性建立了三阶差分方...     

基于叶端定时的转子叶片裂纹监测与诊断

为了实现转子叶片裂纹非接触式监测，提出基于??0范数的叶端定时欠采样信号稀疏重构算法。针对叶端定时信号欠采样问题，建立欠采样信号稀疏表示模型，利用分块正交匹配追踪算法对叶端定时欠采样信号进行分段重构，进而监测在变转速非稳态工况下转子叶片动频的变化情况，据此判断转子叶片是否产生裂纹。针对应变片信号间隔采样的特点，研究基于同步提取变换的时频分析方法，以提高时频图的时频聚集性。开展旋转叶片裂纹扩展试验，同时采用叶端定时和应变片系统测量叶片振动。对比叶端定时和应变片的分析结果，二者均可在裂纹叶片断裂之前至少20min，监测到裂纹叶片和正常叶片的动频发生分离的现象，即裂纹萌生致使叶片动频发生偏移，表明所提出的叶端定时欠采样信号重构方法能够实现转子叶片裂纹的监测与诊断。     

基于遗传算法优化小波网络的柔性喷管力矩特性辨识方法

为准确辨识负载力矩并提高主动加载负载模拟的真实度，使用了一种基于遗传算法优化的神经网络辨识方法。使用小波分析方法对测试信号进行预处理，将消噪与分解后得到的信息作为神经网络训练的扩充样本，提高了辨识精度。使用遗传算法选择最优输入信息、网络结构和隐含层规模，加快网络收敛速度并简化计算过程，实现对柔性喷管力矩的快速准确辨识。仿真结果表明该辨识方法可以准确地描述柔性喷管在典型测试信号激励下的力矩特性，平均辨识误差为2%，对于实现精确主动加载控制和验证伺服控制性能具有重要意义。     

基于运行模态的复合材料梁脱层损伤识别

为了实现复合材料结构损伤的定位与定量识别,利用传递率函数的运行模态分析方法探讨了复合材料梁无损检测方法,通过对加速度传递函数的最小二乘拟合,得到结构的模态频率和阻尼,对传递率函数矩阵奇异值分解,得到结构的振型.运用曲率模态(CMS)和曲率模态变化率(CMSI)作为损伤指标,对具有单损伤、多损伤和不同损伤程度的复合材料梁...     

锯切圆柱形42CrMoA的声发射监测试验

针对锯切加工中的带锯条磨损状态未知、锯切质量下降等问题，本文研究了新试制的FTCUT双金属带锯条锯切圆柱形42CrMoA合金结构钢过程中锯切行为的变化，探讨了声发射信号与单刀锯切过程、锯齿磨损和锯切表面质量变化的关联。研究表明：锯切圆柱材料时随着锯切深度的变化，参与齿数先增大后减小，带锯条约束条件不断变化，进而影响着声发射信号幅值和锯切表面质量；基于时域分析可知锯齿磨损过程分为磨合磨损、快速磨损、稳定磨损三个阶段；基于FFT频域分析发现当频谱图出现多个主频，且第一主频幅值下降时，表明声发射信号来源比较分散，即可认定带锯条失效；基于小波包频域分析发现第7频段信号占比最高，最能反映工件表面质量的变化，其波动情况与锯切表面的微纹和波纹情况可建立一定的联系。因此，可基于声发射信号时域和频域分析判断锯齿磨损和锯切表面恶化情况，为锯齿磨损和锯切表面质量的在线监测提供一定的思路。     

An online ensemble semi-supervised classification framework for air combat target maneuver recognition

Online target maneuver recognition is an important prerequisite for air combat situation recognition and maneuver decision-making. Conventional target maneuver recognition methods adopt mainly supervised learning methods and assume that many sample labels are available. However, in real-world applications, manual sample labeling is often time-consuming and laborious. In addition, airborne sensors collecting target maneuver trajectory information in data streams often cannot process information in real time. To solve these problems, in this paper, an air combat target maneuver recognition model based on an online ensemble semi-supervised classification framework based on online learning, ensemble learning, semi-supervised learning, and Tri-training algorithm, abbreviated as Online Ensemble Semi-supervised Classification Framework (OESCF), is proposed. The framework is divided into four parts: basic classifier offline training stage, online recognition model initialization stage, target maneuver online recognition stage, and online model update stage. Firstly, based on the improved Tri-training algorithm and the fusion decision filtering strategy combined with disagreement, basic classifiers are trained offline by making full use of labeled and unlabeled sample data. Secondly, the dynamic density clustering algorithm of the target maneuver is performed, statistical information of each cluster is calculated, and a set of micro-clusters is obtained to initialize the online recognition model. Thirdly, the ensemble K-Nearest Neighbor (KNN)-based learning method is used to recognize the incoming target maneuver trajectory instances. Finally, to further improve the accuracy and adaptability of the model under the condition of high dynamic air combat, the parameters of the model are updated online using error-driven representation learning, exponential decay function and basic classifier obtained in the offline training stage. The experimental results on several University of California Irvine (UCI) datasets and real air combat target maneuver trajectory data validate the effectiveness of the proposed method in comparison with other semi-supervised models and supervised models, and the results show that the proposed model achieves higher classification accuracy.     

带落角落速约束的导弹虚拟期望落角末制导律

为解决导弹末制导阶段同时考虑落角和落速约束时带来的过载需求大、落速散布广的问题,提出一种基于虚拟期望落角的末制导律。首先,提出虚拟期望落角的概念,设计过渡函数降低末制导初期过载需求；然后,分析过渡函数各参数对落角、落速影响,设计预测-校正算法计算期望参数；为了提高预测效率与精度,使用深度神经网络离线训练弹道数据集。实际飞行中,基于扩展卡尔曼滤波在线辨识气动参数摄动,提高算法的适应性。蒙特卡洛仿真结果表明,所提出的算法能够降低末制导初期过载需求。在满足落角约束与位置精度的前提下,落速控制精度在±15 m/s以内。     

基于视惯融合的大型空间碎片质心位置辨识

大型自由翻滚碎片的质心是在轨操作基坐标系下的不动点，也是碎片连体基下动力学参数向卫星坐标系转换的基准，对其精确识别是提高碎片动力学参数辨识精度的关键。提出基于惯性单元测量数据与双目视觉定位数据融合的大型空间碎片质心位置识别方法。基于无力矩欧拉方程，获取附着到空间碎片表面的惯性单元间转换关系，利用该转换关系对惯性单元冗余测量数据优化，再优化求解惯性单元到质心点距离；利用双目视觉获取惯性单元上标记点动态坐标，再利用惯性单元到质心点距离，基于三点定位原理识别大型空间碎片的质心位置。以加入高斯白噪声的惯性单元与双目视觉测量数据进行仿真，结果表明优化解算后惯性单元实时测量数据的误差降低到1%以下，解算的质心位置三轴误差小于0.47mm；开展了地面试验，结果表明，解算的质心位置三轴误差小于0.49mm。仿真和试验证明，该方法能够为大型空间碎片的消旋、捕获任务提供准确的数据基准。