BCM的多阶段译码算法

针对在某些通信系统中对译码速度较高的需求,基于分组编码调制（BCM,Block Coding Modulation）的多级结构,给出了一种多级BCM的多阶段译码算法:从BCM中第一级分量码开始,根据BCM的级数逐阶段进行译码,针对每级分量码,采用软判决最大似然译码,直到最后一级分量码,从而降低了译码复杂度.仿真结果表明,与传统Viterbi译码算法相比,提出的多阶段译码算法的误码性能与其相当,信噪比较小时甚至更好,但译码复杂度远低于传统Viterbi译码方法.      

基于DFR法的复杂载荷谱的等损伤简化方法

载荷谱简化是缩短疲劳试验时间的有效方法。本文在综合考虑三参数S-N曲线的全寿命适应性、细节疲劳额定(DFR)法计算简单可靠以及等损伤折算法不改变总损伤特点的基础上,提出了一种载荷谱的折算简化方法。该方法首先以块谱中某一载荷水平与最大载荷水平一次循环造成损伤的比γ作为判断依据,设定损伤比门槛值γac,以确定折算载荷,其中损伤计算采用了三参数S-N曲线和DFR法;其次考虑谱型对疲劳试验结果的影响,引入块谱形状因子,以减少简化谱改变谱型而对疲劳损伤及疲劳寿命分散性带来的影响;最后引入了疲劳极限附近小载荷的处理方法,形成了等损伤载荷谱简化方法。试验证明,原载荷谱与简化谱的损伤基本不变,但试验时间可大大缩短。     

无人直升机多天线通信信道建模及性能分析

在单天线航空信道模型的基础上,提出了一种无人直升机多天线通信信道的统计模型.分析了信道的时延扩展、多普勒扩展和角度扩展特性,研究了机身对天线的遮挡效应及多天线信号之间的干涉问题.根据模型特点建立了一种准确有效的仿真模型,给出了延迟功率谱、空间相关性、多普勒功率谱和机身遮挡效应的仿真方法.利用该模型在途中飞行、任务区盘旋和起飞/降落这3种飞行状态下对单天线和多天线信道的误码性能进行了对比仿真分析,验证了差分空时编码在无人直升机多天线通信系统中的良好性能.该模型可用于无人直升机多天线通信系统的调制和编码技术研究.     

拉普拉斯白噪声下的分组Turbo码

目前,传统航空测控通信所采用的纠错码大多是建立在高斯信道基础上的。然而,航空测控环境中不可避免存在着多种尖锐的噪声,测控通信纠错码的可靠性能在非高斯信道中尚未得到充分的研究。分析了一类国际航空遥测的分组Turbo码（BTC）在拉普拉斯白噪声信道下的译码和性能。将传统Chase迭代译码算法引入到拉普拉斯白噪声信道中,建立相应的数学模型,同时,基于该数学模型设计了3种不同的译码接收器下的BTC译码方案。仿真结果验证了该数学模型的正确性与可行性,在误码率为10^-4时最佳译码方案相比于硬限幅接收机有3.7 dB的增益,相比原有的高斯信道下的接收机仅有0.6 dB的性能损失。     

行列式的值等于零的充要条件

利用行列式的一些性质，推导出行列式值为零的充要条件，提出一种简捷的解题方法。并举例说明其应用。     

某型涡轴发动机研制过程中尾喷管漏油机理分析

某型涡轴发动机在研制过程中屡次出现尾喷管漏油故障。在滑油系统原理分析的基础上,建立滑油泄漏故障树,对泄漏原因逐层剖析,并结合分解检查、零件计量等手段对发动机各相关部件和滑油泵进行详细分析,确定故障原因。针对影响因素,提出相应的整改措施。     

基于BDS-GD的低截获概率雷达信号识别

针对在信号特征提取与识别中使用双谱估计数据量大、维度高的问题,提出了双谱对角切片（BDS）与广义维数（GD）相结合的识别方法。通过提取信号双谱对角切片减少数据量,并利用多重分形理论中的广义维数降低数据维度,对切片内部特性进行细微描述,基于距离测度提出特征评价指标,从而选出最具有区分度的3个阶数对应的广义维数作为特征向量,输入到最小二乘支持向量机中进行分类识别。使用4种低截获概率（LPI）雷达信号作为待识别信号,仿真结果表明,本文方法提取的信号特征在特征空间中有良好的聚集性和离散性,在0 dB信噪比下,识别准确率能达到92.2%,与选取的其他方法对比说明其具有很好的识别性能。      

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...     

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.