基于小波模糊神经网络的陀螺仪故障诊断技术

为了提高陀螺仪故障检测灵敏度，分析了陀螺漂移特性，指出随机漂移是影响陀螺仪精度的主要漂移误差，也是影响性能可靠性的主要因素．针对此，提出了一种小波模糊神经网络故障诊断模型．此模型运用串联方式将小波分析、模糊逻辑和神经网络融合在一起，充分发挥它们各自的优点，并对陀螺仪实测数据仿真．仿真结果表明，采用小波分析提取陀螺的常值漂移，简单有效；运用神经网络对陀螺的误差和故障建模，使故障诊断具有自适应、自学习的能力，并互增强了故障诊断的容错能力；将模糊逻辑用于判决中，使判决结果更加可靠．     

飞行器三轴姿态测量方法

介绍了到目前为止常用飞行器三轴姿态测量敏感器.详述了这些姿态敏感器的工作原理.根据这些原理分析了它们的优缺点,并比较了它们的性能指标.给出了具有指导意义的结论.     

某型末制导炮弹惯导陀螺动力学分析与仿真

目前国内外学者将惯导陀螺连同末制导炮弹弹体视作单刚体来研究,对于惯导陀螺内外框摆动角的确定是通过弹体坐标系、惯导陀螺坐标系和地面坐标系之间的转换关系推导出来的,并不能直接反应惯导陀螺内外框摆动的动力学规律.为了揭示惯导陀螺内外框摆动角变化的内在规律,得到多刚体条件下末制导炮弹角运动模型,首先进行基本假设、建立坐标系,其...     

捷联惯导用挠性陀螺静态漂移误差建模

运用极轴翻滚试验,研究了捷联惯导系统使用的动力调谐式挠性陀螺的静态漂移误差的建模方法。仿真分析和试验验证表明,本方法具有建模准确、估计参数较多,对试验数据异常值不敏感等优点,具有较强的实用性。     

一种基于DAVAR的FOG随机信号处理新方法

为定量描述光纤陀螺(FOG)测试信号中各误差分量的动态变化过程,分析了多个随机信号处理方法的优缺点,指出了常用Allan方差分析不能辨识FOG信号非平稳性因素的不足,提出采用动态Allan方差(DAVAR)对FOG信号动态特性进行分析,采用二维表示法定量描述FOG各主要噪声项变化过程。实验结果表明,DAVAR能够反映FOG数据的非平稳特性;利用二维表示的方法,可准确地反映动态误差中各噪声项的变化特征。新的方法可作为FOG信号非平稳性分析的一种定量评价工具,为研究各干扰因素对FOG组成系统精度的影响提供理论参考和评价依据。     

Application of interacting multi-model algorithm in gyro signal processing

There is one problem existing in gyroscope signal processing,which is that single models can＇ t adapt to change of carrier maneuvering process.Since it is difficult to identify the angular motion state of gyroscope carriers,interacting multiple model （IMM） is employed here to solve the problem.The Kalman filter-based IMM （IMMKF） algorithm is explained in detail and its application in gyro signal processing is introduced.And with the help of the Singer model,the system model set of gyro outputs is constructed.In order to demonstrate the effectiveness of the proposed approach,static experiment and dynamic experiment are carried out respectively.Simulation analysis results indicate that the IMMKF algorithm is excellent in eliminating gyro drift errors,which could adapt to the change of carrier maneuvering process well.     

神经网络时间序列预测及其工程应用

基于时间序列分析的方法,针对船用捷联陀螺的具体特性,提出了一种神经网络对间序列预测及建模方法,并对某捷联航姿系统中所用陀螺漂移数据进行了神经网络建模尝试。     

旋转自动补偿捷联惯导系统技术研究

利用旋转法补偿陀螺漂移是提高捷联惯导系统精度的有效途径之一。由于旋转的引入,惯性测量单元中陀螺的常值漂移将被调制成周期性信号,通过积分运算可以有效地消除常值陀螺的漂移影响。提出了一种新的单轴旋转调制方案,对该方案进行了理论推导、分析和仿真。与以往的单轴旋转方式及未采用旋转方式时的导航误差进行了比较,结果表明本方案可以消除所有方向上陀螺常值漂移的影响,从而大大提高位置和姿态精度。     

Dynamic model and simulation of aseismatic system about laser gyro strapdown inertial measure assembly based on MSTMM

The precision of the laser gyro used in tactical missiles is poor because of dithering frequency,actuating by vibration,shock and overload in dynamical environment.This paper introduces the transfer matrix method of the multibody system(MSTMM),establishes the dynamic model of the laser gyro strapdown inertial measure assembly aseismatic system,and analyzes the precision affected by dithering of the laser gyro and shocking of the tactical missile.And the dynamic response of the laser gyro strapdown inertial measure assembly aseismatic system is obtained by simulating the multibody system model.The simulation result indicates a theoretical idea to design the vibration isolation for the laser gyro strapdown inertial measure assembly.     

IUKF neural network modeling for FOG temperature drift

A novel neural network based on iterated unscented Kalman filter （IUKF） algorithm is established to model and com- pensate for the fiber optic gyro （FOG） bias drift caused by temperature. In the network, FOG temperature and its gradient are set as input and the FOG bias drift is set as the expected output. A 2-5-1 network trained with IUKF algorithm is established. The IUKF algorithm is developed on the basis of the unscented Kalman filter （UKF）. The weight and bias vectors of the hidden layer are set as the state of the UKF and its process and measurement equations are deduced according to the network architecture. To solve the unavoidable estimation deviation of the mean and covariance of the states in the UKF algorithm, iterative computation is introduced into the UKF after the measurement update. While the measure- ment noise R is extended into the state vectors before iteration in order to meet the statistic orthogonality of estimate and mea- surement noise. The IUKF algorithm can provide the optimized estimation for the neural network because of its state expansion and iteration. Temperature rise （-20-20℃） and drop （70-20℃） tests for FOG are carried out in an attemperator. The temperature drift model is built with neural network, and it is trained respectively with BP, UKF and IUKF algorithms. The results prove that the proposed model has higher precision compared with the back- propagation （BP） and UKF network models.