Optimal robust non-fragile Kalman-type recursive filtering with finite-step autocorrelated noises and multiple packet dropouts

In this paper, the optimal robust non-fragile Kalman-type recursive filtering problem is studied for a class of uncertain systems with finite-step autocorrelated measurement noises and multiple packet dropouts. The system state, measurement output and filter parameters are all subject to stochastic uncertainties or multiplicative noises, where the measurement noises are finite-step autocorrelated. When there exist multiple packet dropouts in the system output, the original system is converted into an auxiliary stochastic uncertain system by the augmentation of system states and measurements. The process noises and measurement noises of the auxiliary system are shown to be finite-step autocorrelated and cross-correlated. Then, a robust non-fragile Kalman-type recursive filter is designed that is optimal in the minimum-variance sense. The proposed filter is not only robust against the uncertainties in the system model and measurement model, but also non-fragile against the implementation error with the filter parameters. Simulation results are employed to demonstrate the effectiveness of the proposed method.     

Robust adaptive filtering method for SINS/SAR integrated navigation system

This paper presents a new robust adaptive filtering method for SINS/SAR (Strap-down Inertial Navigation System/Synthetic Aperture Radar) integrated navigation system. This method adopts the principle of robust estimation to adaptive filtering of observational data. A robust adaptive filter is developed to adaptively determine the covariance matrix of observation noise, and adaptively adjust the covariance matrix of system state noise according to the adaptive factor constructed based on predicted residuals. Experimental results and comparison analysis demonstrate that the proposed method cannot only effectively resist disturbances due to system state noise and observation noise, but it can also achieve higher accuracy than the adaptive Kalman filtering method.     

散乱点云自适应滤波算法

提出一种散乱点云自适应滤波算法,该算法采用改进的R*-树组织散乱点云的拓扑近邻关系,基于该结构快速准确获取局部型面参考数据,自适应调节二维高斯分布的数字特征计算滤波权值,计算局部型面参考数据对原始型面数据的影响因子,以此作为权值计算各点滤波后的坐标,采用加权平均方法实现散乱点云的自适应滤波.实验证明该算法可有效提高点云的滤波效率,在保留原始型面特征的基础上,减小点云的随机误差,提高光顺性.     

一种简化的机载MIMO雷达杂波特征相消器

针对机载多输入多输出(MIMO)雷达杂波抑制问题,提出一种简化的杂波特征相消器(EC).根据杂波在空时二维平面的先验分布离线构造杂波子空间.以此替代由协方差矩阵特征值分解(EVD)得到的杂波子空间,从而将最优权简化为一个确知投影矩阵与目标信号空时二维导向矢量的乘积,避免了传统EC方法中复杂的协方差矩阵估计和EVD运算,...     

涡轴发动机自适应混合诊断模型高斯加权聚类方法

针对整个飞行包线内涡轴发动机健康参数估计问题,提出基于高斯加权聚类的机载自适应混合模型建立方法.机载自适应混合模型由卡尔曼滤波器和神经网络组成,由于飞行数据样本庞大,采用高斯加权模型对涡轴发动机飞行数据进行实时聚类,利用聚类数据更新神经网络权重,并实现自适应混合模型工作范围的自动扩展.仿真结果表明了该方法的有效性,采用...     

多传感器加权观测融合自适应UKF滤波器

对于带有相同观测方程和未知噪声统计的非线性多传感器系统,提出了一种基于Sage-Husa估计的自适应UKF滤波算法.该算法利用导出的平稳随机序列的相关函数估计系统观测噪声方差统计R(j),并证明了其收敛性.进而利用Sage-Husa估计算法得到自适应UKF滤波算法.该方法避免了传统Sage和Husa的自适应滤波算法不能处理Q和R均未知的系统的局限性.为了将多传感器信息加以充分利用,提高滤波精度,本文利用加权最小二乘法(WLS),实现了多传感器加权观测融合自适应UKF滤波器.一个带3传感器非线性系统的仿真例子说明了该算法的有效性.     

基于电磁矢量传感器阵列的广义旁瓣相消器

提出一种基于特殊电磁矢量传感器阵列的广义旁瓣相消器。首先在理论层面推导得到广义旁瓣相消器最优权矢量;针对信号是否在偶极子均匀圆阵处发生极化失配的四种情况进行讨论,进而明确阵列广义旁瓣相消器的适用场合;最后对广义旁瓣相消器和Capon波束形成器进行性能对比。理论分析和仿真实验表明,将广义旁瓣相消器推广到电磁矢量传感器阵列...     

嫦娥五号月面上升和下降段实时轨道确定

针对月球探测中月面上升和下降段的实时轨道确定问题,提出基于三向测量的实时自适应当前统计方法。首先,通过自适应当前统计模型描述探测器月面上升下降过程,其次,综合利用三向数据进行测量更新,最后,通过UKF滤波算法完成实时轨道确定。由于自适应当前统计模型具有良好的适应性,该方法能对探测器月面上升和下降段进行有效定位,通过嫦娥五号探测器实际上升和下降过程数据进行测试,结果表明,文章提出的基于三向测量的实时自适应当前统计方法比传统的几何定位方法具有更好的抗差性,对深空目标探测具有一定的工程应用价值。     

降噪技术在瞬态应变测试中的应用研究

主要研究在瞬态环境下应变测试的干扰问题。文章从硬、软两个方面入手,讨论了在爆轰环境下出现的一些典型的干扰现象,分析了干扰源及干扰引入途径,并通过设计改进电路、系统的接地、屏蔽等方法提出了抗干扰措施。采用了小波分析和数字滤波等数字信号处理方法来解决信号的降噪问题。