X波段雷达中基于高阶色散关系的滤波器设计

由于在X波段雷达海浪参数反演过程中,利用已有的带通滤波器反演得到的波高、波周期等参数会出现偏大的问题,提出了一种将高阶色散关系边界与基本色散关系相结合的新滤波器.首先推导证明出高阶色散关系的单调性和有界性,得到其边界方程;其次,结合基本色散关系得到新的滤波器;最后进行仿真实验,并与原带通滤波器的结果进行对比.实验结果表明,新的滤波器不仅能够准确的提取海浪信息,滤除掉噪声信息,而且得到的波高、周期及波向等参数结果优于原带通滤波器.     

基于粒子群优化算法的二阶系统解耦

从数值计算的角度出发研究了二阶系统的解耦问题,通过保持Lancaster结构将二阶系统的解耦问题转化为非线性约束的最优化求解问题.并利用粒子群优化算法求解此约束优化问题,直接给出非奇异的保结构解耦变换.利用Matlab编写程序实现此算法,数值实验结果表明在设定的迭代次数和精度内,此方法确实能实现原始系统的近似解耦.     

基于灰色动态MGM(1,n)模型的舰船纵摇-升沉运动预报

舰船在实际海况中的运动因受到各种因素的影响而非常复杂,六个自由度之间相互耦合构成一个复杂的系统,因此建立描述舰船运动的系统模型并对各自由度运动进行实时预报具有非常重要的意义.灰色MGM(1,n)模型用微分方程的形式表现了一个系统中n个因素对某个因素变化率的影响,可以用于对非线性复杂系统的系统预测.通过对灰色系统理论的学习发现对原始数据用极差变换进行预处理后会更适合MGM(1,n)模型的拟合,为模型预测打下了良好的基础.在充分的理论研究基础上,本文对船模水池试验获得的纵摇、升沉运动数据以及海浪数据进行极差变换后建立灰色MGM(1,n)模型,并对纵摇、升沉进行非线性预报,得到了较好的效果.     

一种基于系统保形解耦的力源识别方法

基于模态分析法的力源时域识别方法中涉及利用模态矩阵将系统方程转化为非耦合形式,这仅对比例阻尼或经典阻尼系统才能实现。文章基于二阶系统保结构解耦方法建立了新的力源识别数学模型。首先利用Lancaster结构建立系统的解耦模型,并求得解耦变换;其次,利用解耦变换推导建立系统非耦合响应模型,并在微小时段内力源为线性变化的假设下,推导出具体的力源识别公式。最后,采用精细逐步积分方法,对模型进行解算,由结构动态响应反求力源的时间历程。数值实验不仅验证了所提方法可以提高识别精度,而且也验证了所提方法对非比例阻尼系统也是有效的,这也是文中方法的重点。     

基于灰色系统理论的滑行艇运动姿态预报(英文)

In order to minimize the harm caused by the instability of a planing craft, a motion prediction model is essential. This paper analyzed the feasibility of using an MGM(1,N) model in grey system theory to predict planing craft motion and carried out the numerical simulation experiment. According to the characteristics of planing craft motion, a recurrence formula was proposed of the parameter matrix of an MGM(1,N) model. Using this formula, data can be updated in real-time without increasing computational complexity significantly. The results of numerical simulation show that using an MGM(1,N) model to predict planing motion is feasible and useful for prediction. So the method proposed in this study can reflect the planing craft motion mechanism successfully, and has rational and effective functions of forecasting and analyzing trends.     

Application of the Grey topological method to predict the effects of ship pitching

Ship motion, with six degrees of freedom, is a complex stochastic process. Sea wind and waves are the primary influencing factors. Prediction of ship motion is significant for ship navigation. To eliminate errors, a path prediction model incorporating ship pitching was developed using the Gray topological method, after analyzing ship pitching motions. With the help of simple introduction to Gray system theory, we selected a group of threshold values. Based on an analysis of ship pitch angle sequences over 40 second intervals, a Grey metabolism GM（1,1） model was established according to the time-series which every threshold corresponded to. Forecasting future ship motion with the GM （1,1） model allowed drawing of the forecast curve with effective forecasting points. The precision of the test results show that the model is accurate, and the forecast results are reliable.