采用UVM的数字抽取滤波器的验证

采用通用验证方法学（UVM）搭建验证平台,以Sigma-Delta A/D转换器中的数字抽取滤波器为验证对象,重点分析UVM验证平台的设计方法.利用该UVM验证平台,产生随机化的测试向量,对数字抽取滤波器模块进行全面的验证,最终达到覆盖率要求.通过与传统定向测试平台的验证结果对比,可以得出使用通用验证方法学的验证效率与验证能力更高.     

低频谐波干扰抑制技术

介绍了几种谐波干扰的抑制技术的补偿机理、拓扑结构、补偿特性及应用前景.并着重介绍了有源电力滤波器的分类、原理和控制方法等.与传统的无源滤波法相比,有源电力滤波技术具有良好的动态跟踪性能,既可补偿谐波也可补偿无功分量.同时电力电子技术、信号检测及处理技术、控制技术和大功率技术的快速发展,为有源电力滤波器的发展提供了广阔的前景.     

利用无线多跳网络振动传感器实现过程监测

本文设计了一款基于无线传感技术的低功耗无线多跳网络振动传感器实现结构三轴振动的过程检测.采用高精度加速度传感器拾取振动信息,通过二阶巴特沃斯有源滤波器进行信号调理,最后通过基于Zigbee的无线传感器网络进行无线多跳网络的组建和数据的发送,进行多传感器数据融合.实验证明本设计具有良好的精度和可操作性,具有数据溯源和存储能力,能够进行连续过程检测.     

两种自适应噪声抵消算法的性能仿真

介绍了自适应噪声抵消系统及其核心部分一自适应滤波器的基本原理和常见算法,并采用LMS、NLMS算法在Matlab平台中进行了系统消噪性能的仿真对比,讨论了不同参数设定对系统性能的影响.结果表明,当其他条件不变时,所选用步长或者滤波器阶数的增加,会造成滤波器自适应学习时间加长,失调系数变大,消噪性能趋于恶化;对于有色噪声的消除,NLMS算法有明显优势和效果,而LMS算法则存在梯度噪声被放大、失真度高等问题.     

基于瞬时线速度的捷联惯导系泊精对准方法

针对捷联惯导系统系泊精对准问题,提出了一种利用舰船瞬时线速度作为参考速度的精对准方法.通过大量试验研究,分析捷联惯导系统输出速度的信号特征,设计数字高通滤波器,从捷联惯导系统速度中提取舰船瞬时线速度信息.建立了适用于系泊条件的捷联惯导系统误差模型,并以计算出的舰船瞬时线速度作为参考速度完成卡尔曼滤波精对准.试验结果表明,所提取出的舰船瞬时线速度误差小于0.08m/s,且新方法的对准精度和重复性明显优于以零速为参考量的对准方法.     

闭环系统辨识的模型结构检验

对于闭环系统辨识的模型结构检验问题,在预测误差辨识法的前提下,从参数估计的统计特性中推导出两概率模型不确定性边界及最优的输入滤波器形式。概率边界及输入滤波器是基于参数估计的渐近正态分布的方差矩阵,该方差矩阵由采样数据估计而得。根据未知参数的渐近方差矩阵内积形式从概率统计意义上构造模型参数及互相关函数的不确定性边界,从优化的角度推导输入滤波器的选取形式。最后用仿真算例验证本文辨识方法的有效性。     

Simple adaptive filtering scheme to improve measurement accuracy of gyroscope on angular motion base

The objective of this work is to improve the measurement accuracy of a gyroscope on a angular motion base with a simple adaptive filter scheme. Two main topics are highlighted in this work. The first topic is to show building a dual-process model employed for the conventional Kalman filter. The second topic is to show developing a modified noise adaptive algorithm when measurement noise and process noise are unknown. The experimental results are presented to show that the simple adaptive filtering scheme outperforms the other conventional scheme in this paper in terms of noise reduction.     

Constrained submap algorithm for simultaneous localization and mapping

When solving the problem of simultaneous localization and mapping (SLAM), a standard extended Kalman filter (EKF) is subject to linearization errors and causes optimistic estimation. This paper proposes a submap algorithm, which builds a weighted least squares (WLS) constraint between two adjacent submaps according to the different estimations of the common features and the relationship between the vehicle poses in the corresponding submaps. By establishing the constraint equation after loop closing, re-linearization is implemented and each submap’s reference frame tends to its equilibrium position quickly. Experimental results demonstrate that the algorithm could get a globally consistent map and linearization errors are limited in local regions.     

A novel approach to design robust fault detection filter for networked control systems

In this paper, an approach for designing robust fault detection filter (RFDF) of networked control systems (NCSs) with unknown inputs is studied. The design aims at implementing the optimal trade-off between robustness of unknown inputs (including the item produced by networked-induced delay) and sensitivity of fault. The key design issue is to introduce an optimal fault detection filter based on NCSs with the control law compensation as the reference residual model of NCSs and to formulate the RFDF design as a model-matching problem. By applying H _∞ optimization technique, linear matrix inequality (LMI) approach is given to solve the model-matching problem. The validity of the proposed approach is shown by a numerical example.      

基于EKF的神经网络在网络流量预测中的应用

在网络资源有限的情况下,建立合理的网络流量预测模型,并根据其预测结果及时做出控制决策或调整措施,对网络性能和服务质量的提高均有重要意义.根据网络流量的时变、非线性特点建立一个时间相关的流量预测模型,预测和分析网络流量状况,并利用人工神经网络在非线性建模方面的优势,给出了基于EKF算法的前馈神经网络的结构设计及学习算法.最后在Matlab环境下使用该预测模型对网络流量进行了仿真,结果表明该模型具有较好的自适应性和较高的预测精度.