使用Trimble Geomatics Office等商业随机软件应注意的几个问题

通过对Trimble Geomatics Office等随机软件的实际应用,提出一些数据质量控制措施.根据随机软件网平差功能的特点,通过对所有基线网和独立基线网的对比分析,得出选择独立基线网平差更合理、测量结果更可靠的结论.     

Model-free adaptive optimal design for trajectory tracking control of rocket-powered vehicle

An adaptive optimal trajectory tracking controller is presented for the Solid-Rocket-Powered Vehicle (SRPV) with uncertain nonlinear non-affine dynamics in the framework of adaptive dynamic programming. First, considering that the ascent model of the SRPV is non-affine, a model-free Single Network Adaptive Critic (SNAC) method is developed based on the dynamic neural network and the traditional SNAC method. This developed model-free SNAC method overcomes the limitation of the traditional SNAC method that can only be applied to affine systems. Then, a closed-form adaptive optimal controller is designed for the non-affine dynamics of SRPVs. This controller can adjust its parameters under different flight conditions and converge to the approximate optimal controller through online self-learning. Finally, the convergence to the approximate optimal controller is proved. The theoretical analysis of the uniformly ultimate boundedness of the tracking error is also presented. Simulation results demonstrate the effectiveness of the proposed controller.     

风洞无线智能传感器网络及无线测量技术研究

风洞试验现场，特别是大型连续式风洞试验现场的电磁环境非常恶劣，而风洞实验中的测量信号又是毫伏级的微弱信号，采用传统测量方式难以克服恶劣的电磁环境对测量系统的干扰。对于运动物体或无法布线的试验环境进行参数测量时，传统测量方式完全不起作用。针对上述问题，笔者对风洞无线智能传感器网络和无线测量技术进行了研究，采用ZIGBEE技术实现了风洞无线智能传感器网络和基于该网络的无线测量模块。极大地增强了测量系统的抗干扰能力，提高了风洞试验的精细化程度，降低了测量系统的经济成本。     

一种新的基于双控制信道Ad Hoc网络媒体接入控制协议

提出了一种新的基于双控制信道的AdHoc网络媒体接入控制(MAC)协议。该协议在两个相互正交的子信道上交换RTS/CTS控制分组,解决了RTS丢失问题、死锁问题以及多信道隐藏终端问题,并提高了协议的空间复用度。实验结果表明,和已有的多信道的接入协议相比,在网络负载较重和节点密度较大的情况下,新协议提高了网络的吞吐量。     

Modeling and Algorithm Application of Weapon Assignment System

In order to improve weapon assignment(WA)accuracy in real scenario,an artificial neural network(ANN)model is built to calculate real-time weapon kill probabilities.Considering the WA characteristic,each input representing one assessment index should be normalized properly.Therefore,the modified WA model is oriented from constant value to dynamic computation.Then an improved invasive weed optimization algorithm is applied to solve the WA problem.During search process,local search is used to improve the initial population,and seed reproduction is redefined to guarantee the mutation from multipoint to single point.In addition,the idea of vaccination and immune selection in biology is added into optimization process.Finally,simulation results verify the model′s rationality and effectiveness of the proposed algorithm.     

集群卫星网络信息拓扑控制技术探讨

集群卫星网络所具有的高动态拓扑、信息异构及灵活组网的特性,使其对信息层面的拓扑控制提出了新的挑战,需要在现有的卫星网络拓扑控制技术的基础上进一步研究。在深入解析集群卫星网络信息拓扑控制内涵的基础上,根据集群卫星网络特点,提出通用的集群卫星网络信息拓扑模型及星地拓扑控制策略,并对现有的卫星拓扑控制算法进行分类说明,根据拓扑的分类方法,分析不同卫星拓扑控制技术在运用于集群卫星网络中的限制条件,最后针对应用需求及技术特点,指出集群卫星网络拓扑控制技术的发展趋势及研究方向。     

一种弹道修正弹SINS任意滚转角快速粗对准方法

针对弹道修正弹药出炮口后滚转角处于随机状态,捷联惯导系统(SINS)失准角过大时卡尔曼滤波收敛困难的问题,提出在卫星拒止环境下利用神经网络快速估计初始滚转角的改进方法。在炮口处布设少量导航信标,建立反向传播(BP)神经网络拟合初始滚转角与观测量间的非线性映射模型。针对信标辅助下姿态弱可观的问题,引入惯导测量参数作为输入,提高网络估计精度。采用主成分分析法进行特征提取,简化网络结构。仿真结果表明,与基于非线性卡尔曼滤波的对准方法相比,本算法可实现任意滚转角下的快速粗对准;对射角、初始俯仰角误差未在训练范围内以及存在布设误差等场景也进行了测试,与未优化的BP网络相比,对准精度更高,鲁棒性更好。     

基于ANFIS的舵阻横摇系统

在MATLAB环境下建立了舵阻摇系统的非线性模型．基于所建立的非线性模型和ANFIS控制算法进行了仿真实验。运用Simulink工具箱得到仿真曲线．通过仿真曲线可以看出此控制器具有良好的鲁棒性能．通过对比减摇前后的仿真曲线得出,ANFIS算法对于舵阻摇系统的拉制有效,减摇率为23%左右,减摇效果较好。     

On developing data-driven turbulence model for DG solution of RANS

High-order Discontinuous Galerkin(DG) methods have been receiving more and more attentions in the area of Computational Fluid Dynamics(CFD) because of their high-accuracy property. However, it is still a challenge to obtain converged solution rapidly when solving the Reynolds Averaged Navier–Stokes(RANS) equations since the turbulence models significantly increase the nonlinearity of discretization system. The overall goal of this research is to develop an Artificial Neural Networks(ANNs) model with low complexity acting as an algebraic turbulence model to estimate the turbulence eddy viscosity for RANS. The ANN turbulence model is off-line trained using the training data generated by the widely used Spalart–Allmaras(SA) turbulence model before the Optimal Brain Surgeon(OBS) is employed to determine the relevancy of input features.Using the selected relevant features, a fully connected ANN model is constructed. The performance of the developed ANN model is numerically tested in the framework of DG for RANS, where the‘‘DG+ANN" method provides robust and steady convergence compared to the ‘‘DG+SA" method. The results demonstrate the promising potential to develop a general turbulence model based on artificial intelligence in the future given the training data covering a large rang of flow conditions.     

基于深度学习的地空导弹发射区拟合算法

目前地空导弹发射区的拟合算法主要是多项式拟合法和BP神经网络拟合法。多项式拟合法存在函数形式难以确定、函数范围不易分段等问题，且拟合精度较低；传统神经网络方法要想达到较高精度，需要大量的隐层节点，且在隐层节点数增加到一定程度后，训练变得十分困难且精度很难继续提高。同时，传统神经网络需要大量的标签数据，进一步增大了实际应用的难度。为此，基于深度学习理论，设计了一种基于堆栈稀疏自编码器（SSAE）的深度拟合网络（DFN），并给出了相应的训练策略。仿真实验表明其相比传统算法具有更小的拟合误差优势。所设计的深度稀疏自编码器网络可以克服多项式拟合和传统神经网络的不足，不仅可以在大量无标签数据和少量标签数据条件下进行学习训练，而且可以进一步提升地空导弹发射区的拟合精度。