基于模糊神经网络的自适应滤波方法仿真研究

提出了一种模糊自适应卡尔曼滤波算法。该算法基于模糊规则，根据新息相关性，自适应调整测量噪声方差R，有效的解决了噪声的统计特性与实际不符时，滤波器发散的现象。同时，利用Elman网络作为误差估计器，补偿模糊自适应卡尔曼滤波器的估计误差。仿真结果表明，两种方法结合，可以有效地防止滤波器发散，缩小实际的滤波误差，提高滤波精度，实现滤波器参数的在线改进。     

中国公司治理的路径依赖——理论与实证分析

1Introduction　Now ,in orbitspacecraftsdeveloprapidlyinqualityandquantity,whilethegroundTT&Cresourceisrelativelylimi ted .Therefore ,torealizespacecraftautonomousnavigationandpositioningseemsespeciallytobeimportant.Itisoneoftheimportantaspectstoincreasethein orbitspacecraftself containedability .　ExtensiveattentionshavebeenpaidtotheapplicationofGPSinthisarea,andquiteanumberofachievementshavebeengained ,butitisonlythebeginning .　FortheutilizationofGPSinspacecraftnavigationandpo sitioning …     

基于折扣高斯粒子滤波器的桥梁可靠性动态预测

桥梁健康监测(BHM)系统在长期运营中积累了大量信息,如何利用这些信息动态预测结构可靠性已成为BHM领域的关键科学问题之一.为合理预测桥梁的动态可靠性,应用BHM系统日常监测的极值应力数据,建立带有最优折扣因子的动态线性模型,结合高斯粒子滤波器给出折扣高斯粒子滤波器预测算法,分别对日常监测极值应力的一步向前预测分布参数和状态变量后验分布参数进行修正预测,并基于此,采用一次二阶矩(FOSM)方法预测桥梁的动态可靠性,结合桥梁实测数据对所提方法进行了验证分析,为桥梁预防性养护维修决策提供理论基础.     

有色观测噪声下的多变量设置调整策略

针对存在初始设置偏差的离散的多变量生产制造过程，研究了观测噪声服从Auto-Rgressive （AR）模型的情况下，考虑调整花费成本为二次型函数时的设置调整问题，在建立过程状态空间方程的基础上，利用卡尔曼滤波方法在线估计过程的状态变量，根据随机二次型最优控制理论，得到了使过程质量损失最小的最优调整策略. 通过算例解释了最优调整策略的实现方法，并进行了仿真验证，结果表明，得到的调整策略与观测噪声为白噪声时的质量调整策略相比，能更好地减少过程总体质量损失.     

New rapid transfer alignment method for SINS of airborne weapon systems

Transfer alignment is an effective alignment method for the strapdown inertial navigation system （SINS） of airborne weapon systems. The traditional transfer alignment methods for large misalignment angles alignment use nonlinear transfer align- ment models and incorporate nonlinear filtering. A rapid transfer alignment method with linear models and linear filtering for ar- bitrary misalignment angles is presented. Through the attitude quaternion decomposition, the purpose of transfer alignment is converted to estimate a constant quaternion. Employing special manipulations on measurement equation, velocity and attitude linear measurement equations are derived. Then the linear trans- fer alignment model for arbitrary misalignment angles is built. An adaptive Kalman filter is developed to handle modeling errors of the measurement noise statistics. Simulation results show feasibili- ty and effectiveness of the proposed method, which provides an alternative rapid transfer alignment method for airborne weapons.     

Self-alignment of full skewed RSINS：observability analysis and full-observable Kalman filter

Traditional orthogonal strapdown inertial navigation sys-tem （SINS） cannot achieve satisfactory self-alignment accuracy in the stationary base： taking more than 5 minutes and al the iner-tial sensors biases cannot get ful observability except the up-axis accelerometer. However, the ful skewed redundant SINS （RSINS） can not only enhance the reliability of the system, but also improve the accuracy of the system, such as the initial alignment. Firstly, the observability of the system state includes attitude errors and al the inertial sensors biases are analyzed with the global perspective method： any three gyroscopes and three accelerometers can be assembled into an independent subordinate SINS （sub-SINS）;the system state can be uniquely confirmed by the coupling connec-tions of al the sub-SINSs;the attitude errors and random constant biases of al the inertial sensors are observable. However, the ran-dom noises of the inertial sensors are not taken into account in the above analyzing process. Secondly, the ful-observable Kalman filter which can be applied to the actual RSINS containing random noises is established; the system state includes the position, ve-locity, attitude errors of al the sub-SINSs and the random constant biases of the redundant inertial sensors. At last, the initial self-alignment process of a typical four-redundancy ful skewed RSINS is simulated： the horizontal attitudes （pitch, rol ） errors and yaw error can be exactly evaluated within 80 s and 100 s respectively, while the random constant biases of gyroscopes and accelero-meters can be precisely evaluated within 120 s. For the ful skewed RSINS, the self-alignment accuracy is greatly improved, mean-while the self-alignment time is widely shortened.     

BUF634在高频滤波器中设计方法的实现与改进

本文介绍了某型飞机机载高频滤波器的设计方法与工作原理,在此基础上对芯片BUF634的使用方法进行了阐述,并对实际应用中存在的问题进行分析,提出了改进方法。实验验证证明,该方法简单有效,适用范围广。     

基于卡尔曼滤波的多滑模面电动舵机控制

针对导弹用力矩电机的位置反馈控制,提出一种采用多滑模面自适应变结构控制法,采用卡尔曼滤波器估计状态,消除测量噪声,降低输出抖动,对多滑模面控制率的稳定与收敛性进行适当证明,通过仿真验证算法的有效性。     

一种参数自调整风电功率预测模型

为了提高风电功率预测精度,提出一种参数自调整风电功率预测模型.通过加权递推最小二乘(SWWRLS)方法建立预测模型,侧重当前数据对预测结果的影响,排除了历史数据对预测结果的干扰.模型通过加权递推的方法节省了存储空间,并且提高了模型对外界环境数据变化的自适应性.最后,分别采用支持向量机(SVM)方法、卡尔曼滤波(KF)方法和本文SWWRLS方法,以辽宁省某风电场的真实历史数据进行风电功率预测对比实验,实验结果表明,本文方法建立的模型具有较高的预测精度.     

基于陀螺星光加激光交会雷达的相对姿态确定

针对编队飞行中从飞行器与主飞行器的相对姿态确定问题,提出了基于扩展卡尔曼滤波(EKF)的相对姿态确定方法. 采用修正罗德里格参数(MRPs)作为姿态描述参数避免奇异点. 姿态敏感器采用陀螺 星敏感器 激光交会雷达的配置模式,并且结合相对姿态动力学方程得到相对姿态确定的状态方程,建立起相对姿态确定的EKF模型. 仿真实例表明,EKF状态能在最慢300 s内收敛,MRPs的估计误差在10-5范围以内,该方法正确有效.