空战仿真中交互技术研究

对多机多目标超视距空战仿真中的交互技术进行了研究。针对信息交互问题,提出了直达交换技术和中继交换技术;对于交互同步问题,提出了即时同步技术和实时同步技术。提出了“网络实时交互仿真,结点独立战术分析”概念,使每位飞行员既能亲身参与完整的仿真过程,又能在仿真结束后,方便、独立地进行战术分析。最后,通过典型空战仿真实例的实现,验证了所提出的新概念和关键技术。     

内重力波传播的3维传输函数模式研究

在考虑背景风场及大气耗散的条件下,建立了3维内重力波传输函数数值模式.分析了300 km高度3维传输函数在频率波数域的特性,并以近地面单位脉冲点源为激发源,得到了内重力波在3维空间中的时空分布.讨论了不同时空尺度地面方波源激发的内重力波在电离层高度的能量分布特征.结果表明,(1)对内重力波而言,背景大气相当于一个带通滤波器,只有波动周期和波长分别在15～30 min和200～400 km之间的重力波扰动最容易上传到300km高度;(2)在背景风场的作用下水平面上以同心圆扩散的波阵面以及垂直方向上成漏斗状的波阵面发生了变形,并且逆风方向比顺风方向更有利于声重力波由对流层向电离层高度传播;(3)300km高度对时间尺度和空间尺度分别在20～30 min和150～250 km之间的地面方波源响应的总能量最强.      

基于图像特征分类和RBF网络的两轴车辆动态称重技术

汽车在行驶过程中的总重信号通常由各轴重信号确定,各轴重信号的测试精度取决于对车辆运动参数和振动信号等的精确分析。本文采用径向基函数(Radial basis function,RBF)网络处理轴重信号,针对该网络的泛化能力与拟合精度的矛盾,将车辆按照重量分为大、中、小3种类型,并进行整车建模和网络训练;实际测试过程中,利用汽车俯视图像提取类型特征,然后根据汽车的类型将测试参数输入不同的神经网络进行处理,以静态测量结果为相对真值。仿真结果表明,分类建模比单一混合建模具有更高的测试精度。     

基于小生境遗传自适应RBFN的歼击机故障认定方法

在模式识别领域中,如何实现更高精度的分类一直是个核心问题。本文提出了将自适应RBF神经网络与小生境遗传算法相结合的方法,其中自适应RBF神经网络通过对样本判断,自动实现对RBF网络添加新的隐层节点或者将样本归于已存在的隐层节点所属的类;小生境遗传算法用于寻找最优的网络宽度值。两者相结合最后确定一个隐层节点数与类别数相同的俭省的网络。用歼击机故障数据进行仿真,比较结果表明此方法能实现更高精度的故障认定。     

采用PQLF的不确定模糊系统最优保性能控制

对一类模有界的参数不确定T-S模糊系统的保性能控制问题进行了分析研究。一个二次指标函数表征了闭环系统的性能。采用分段二次Lyapunov函数(Piecewise quadratic Lyapunov function,PQLF)方法,在使闭环系统渐近稳定的前提下以线性矩阵不等式(Linear matrix inequality,LMI)的形式给出了一种鲁棒最优保性能控制律。同时提出了一种新型分段并行补偿(Parallel distributed compensation,PDC)控制策略。该PDC控制器同样反映了模糊系统前提变量的规则结构信息。数值仿真显示,采用该设计方法所得到的闭环保性能值优于传统二次Lyapunov函数(Common quadratic Lyapunov function,CQLF)方法下的闭环保性能值,且系统动态性能良好,鲁棒性强。     

POD降阶算法中对基模态表达的改进

由一系列已知的相近流场解重新组合成一组正交分解(POD)的基模态。利用这些模态的线性组合构造绕一任意翼型的流场,使计算的未知量数目仅为所取相近解流场个数,而传统方法的未知量数目为网格单元数的4倍,因此极大地减少了未知量的数目,将其用于翼型优化,计算量会显著减少。给定的一系列相近流场解,是通过计算快照得到的,这些快照是形成正交模态的根本,直接影响近似计算的准确程度。本文提出了一种新的扰动方法,即通过扰动翼型的关键控制点反求翼型函数得到快照。通过几个已知的翼型解对子空间的投影比较可以看出,该方法的空间表达能力优于常用的加局部凸起的H icks-H enne函数的方法,而且近似计算的准确程度也更好。最后,探讨了正交分解的必要性,阐明利用POD降阶模型方法计算流场可以迅速收敛的内在原因。     

解析壁面函数的可压缩效应修正研究

基于适用于不可压缩流动的解析壁面函数,针对可压缩湍流边界层特征,考虑壁面网格内对流项变化和能量方程中黏性耗散项的影响,提出一种适用于可压缩流动的解析壁面函数.基于二维超声速和高超声速激波湍流边界层干扰流动,完成了粗网格高雷诺数k-ε模型加标准壁面函数、原始解析壁面函数、可压缩修正解析壁面函数和密网格低雷诺数Lauder...     

A variational Bayesian-based robust adaptive filtering for precise point positioning using undifferenced and uncombined observations

In the application of precise point positioning (PPP), especially in the dynamic mode, the classical Kalman filter (KF) usually produces a large number of estimation errors or diverges when there are gross errors in the observation data or unexpected turbulences occur in target motion state or both of them. For such problem, a variational Bayesian (VB)-based robust adaptive Kalman filtering (VB-RAKF) is proposed in this paper. This filter introduces a classification robust equivalent weight function to resist observation gross error and the inverse Wishart prior to model inaccurate process noise covariance matrix (PNCM). To improve the instantaneous accuracy of state estimation, the VB approach is used to obtain better estimations of inaccurate PNCM. Several sets of observation data collected by IGS reference stations and vehicles are employed to check the robustness and positioning accuracy of the VB-RAKF model. The results show that the VB-RAKF algorithm is more robust than the KF, and can effectively resist the gross error in observation data and control state disturbance. In the IGS reference station tests, when compared to the KF, the static positioning accuracies of the VB-RAKF in the north, east and up directions are improved by 13%, 8% and 22%, respectively, and the simulated dynamic positioning accuracies of the VB-RAKF in the north, east and up directions are improved by 19%, 9% and 21%, respectively. The in-vehicle dynamic test verifies that the VB-RAKF outperforms the KF, and shows that the VB-RAKF has better performance than the KF when dealing with observation data which has obvious gross errors, and similar performance as the KF when gross errors are small.     

Reliability estimation of rotary lip seal in aircraft utility system based on time-varying dependence degradation model and its experimental validation

With several attractive properties, rotary lip seals are widely used in aircraft utility system, and their reliability estimation has drawn more and more attention. This work proposes a reliability estimation approach based on time-varying dependence analysis. The dependence between the two performance indicators of rotary lip seals, namely leakage rate and friction torque, is modeled by time-varying copula function with polynomial to denote the time-varying parameters, and an efficient copula selection approach is utilized to select the optimal copula function. Parameter estimation is carried out based on a Bayesian method and the reliability during the whole lifetime is calculated based on a Monte Carlo method. Degradation test for rotary lip seal is conducted and the proposed model is validated by test data. The optimal copula function and optimal order of polynomial are determined based on test data. Results show that this model is effective in estimating the reliability of rotary lip seals and can achieve a better goodness of fit.     

A global empirical orthogonal function model of plasmaspheric electron content

Based on the method for establishing a global plasmaspheric model using observations from COSMIC and MetOp-A orbit determination GNSS receivers, Chen et al. (2017) obtained a global plasmaspheric total electron content product with a spatial resolution of 2.5° × 5° and a time resolution of 4 h. In this paper, we use those global plasmaspheric electron content product in 2008, 2010, 2011, 2012, 2014 for 1446 days to establish a global plasmaspheric empirical model based on empirical orthogonal function (EOF). The model can well characterize the spatiotemporal variation of plasmaspheric electron content (PEC) and the influence of solar radiation on it. Only the first four orders of EOF sequences can characterize the 98.43% features of the original PEC dataset. The principal component coefficient Pk is decomposed twice during modeling, and the combination of trigonometric function and linear function is used to model Pk to characterize the solar cycle, annual cycle, semi-annual cycle and quarter-cycle variation. We compare the PEC model values with the actual observation data, the results show that the empirical PEC model values are highly correlated with the actual observations. The correlation between the two is above 0.96, and the RMS maximum of the difference between the PEC model values and the observed values are 0.70 TECU, and the average of the difference between the PEC model values and the observed values are −0.18 TECU, respectively. In addition, we validate the reliability of the global plasmaspheric model established by two empirical orthogonal function decomposition method using actual observation data, according to the global distribution of the differences between the PEC model values and the observed values in low solar activity and high solar activity, it can be seen that under low solar activity and high solar activity conditions, the model has good adaptability.