基于PSK调制的数字通信信号仿真分析技术

基于PSK调制的数字通信系统在无线通信工程实践中有着广泛的应用，由于受到各种条件的限制，从实际工程系统接收到的PSK数字通信信号在教学科研中不易获得，而PSK调制仿真数据则可以自己产生。在掌握通信原理和仿真技术的基础上利用Matlab实现对BPSK、DPSK、QPSK、DQPSK、8PSK、π/4-DQPSK、OQPSK等数字通信信号源的模拟仿真，可对调制体制、工作频率、传输码率、采样频率、信道特征等参数进行灵活设置，并按要求生成规定格式的DAT数据文件，可作为调制方式识别软件的训练和测试样本。结合模方谱、平方谱、四次方谱、八次方谱等谱线参数特征，同时结合星座图和眼图进行基于PSK调制的数字通信信号的调制体制判别和验证。     

中弧线角度分布对跨音叶栅特性影响研究

中弧线角度分布形式对压气机叶片表面等熵马赫数分布具有重要的影响。为了对比不同压气机叶型的流动特征，采用数值模拟和平面叶栅试验相结合，对两种中弧线角度分布形式的跨音叶型在不同来流工况下的气动特性展开了详细研究。结果表明：在设计点，前加载配合均匀加载的中弧线角度分布叶型能够消除叶片吸力面的局部超音区，与前加载的中弧线角度分布叶型相比，叶片表面等熵马赫数分布更加饱满。其次，前加载配合均匀加载叶型能够推迟叶片吸力面的涡层失稳，减弱了吸力面分离涡与端壁分离涡的耦合作用，进而改善了端区的流动状况，截面平均总压损失系数降低了8.2%。最后，对非设计工况下两种中弧线角度分布的气动特性进行了分析，随着来流马赫数的降低，两种中弧线角度分布叶型的损失差异逐渐减小，均具有较优的气动性能。来流速度对叶片表面等熵马赫数、损失分布和分离面形状特征影响较小，仅影响叶片表面等熵马赫数和损失数值大小。     

Calculating the simultaneous effect of ion dynamics and oscillating electric fields on Stark profiles

We calculate hydrogen line shapes resulting from the simultaneous Stark effect of the plasma microfield and an oscillating electric field. Like laboratory plasmas, many kinds of space plasmas are affected by oscillating electric fields with a magnitude similar to that of the microfield. Here we focus on conditions where we expect that the effect of ion dynamics and oscillating electric are both significant. The combined effect of their dynamics on the quantum emitter is retained by a computer simulation coupled to a numerical integration of the Schrödinger equation. Our calculations are applied for conditions and transitions where significant changes in the line shape allow for a diagnostic of the plasma and oscillating field.     

Quick identification of guidance law for an incoming missile using multiple-model mechanism

A guidance law parameter identification model based on Gated Recurrent Unit (GRU) neural network is established. The scenario of the model is that an incoming missile (called missile) attacks a target aircraft (called aircraft) using Proportional Navigation (PN) guidance law. The parameter identification is viewed as a regression problem in this paper rather than a classification problem, which means the assumption that the parameter is in a finite set of possible results is discarded. To increase the training speed of the neural network and obtain the nonlinear mapping relationship between kinematic information and the guidance law parameter of the incoming missile, an output processing method called Multiple-Model Mechanism (MMM) is proposed. Compared with a conventional GRU neural network, the model established in this paper can deal with data of any length through an encoding layer in front of the input layer. The effectiveness of the proposed Multiple-Model Mechanism and the performance of the guidance law parameter identification model are demonstrated using numerical simulation.