White noise estimation for discrete-time systems with random delay and packet dropout

This paper is concerned with the optimal and suboptimal deconvolution problems for discrete-time systems with random delayed observations. When the random delay is known online, i.e., time stamped, the random delayed system is reconstructed as an equivalent delay-free one by using measurement reorganization technique, and then an optimal input white noise estimator is presented based on the stochastic Kahnan filtering theory. However, tb_e optimal white-noise estimator is timevarying, stochastic, and doesn＇t converge to a steady state in general. Then an alternative suboptimal input white-noise estimator with deterministic gains is developed under a new criteria. The estimator gain and its respective error covariance-matrix information are derived based on a new suboptimal state estimator. It can be shown that the suboptimal input white-noise estimator converges to a steady-state one under appropriate assumptions.     

基于小波包与Elman神经网络的整流电路故障诊断

引入了一种针对电励磁双凸极发电机的整流电路故障的小波和Elman神经网络相结合的诊断方法.根据整流电压波形的畸变规律,将2个以内的二极管故障分为7类共计22种故障元.选用Daubechies小波作为小波基,对实测的整流电压进行小波包分析,利用小波系数,在各个频带上进行小波信号重构.提取全频带能量特征值,并以此构造故障模式向量作为神经网络的输入,实现了故障的分类和定位.以一台840W、12/8极的电励磁双凸极发电机为实验样机,通过采集大量故障信号并进行信号处理,以故障特征值的容差范围作为误差判别标准,力求诊断误差最小化.实验结果表明,该方法对被分析信号的频率波动和幅值变化均具有良好的鲁棒性,是一种有效的故障诊断方法,具有较高的故障诊断正确率.     

一种新型射频识别读写器天线的仿真与设计

提出了一种由两组正交放置的双L天线构成的新型圆极化射频识别读写器天线,有效地克服了平面倒F天线缝隙加载等技术带来的两个谐振点频率相差大、带宽较窄的缺点.利用时域有限差分法对天线仿真和优化,天线的两个频段的中心工作频率分别位于我国分配给RFID技术使用的两个超高频频段内.在ISO/IEC推荐RFID使用频段内的反射损失小于-10dB,天线的相对带宽达到17%以上.优化3dB电桥馈电网络设计,在工作频段内为天线提供等功率、相位差为90°的激励信号,天线具有圆极化特性.仿真和测试结果表明:优化天线设计很好地缓解了天线带宽增加和天线效率下降的矛盾,在我国为RFID分配的频段内具有高的天线效率,并能够工作在ISO/IEC推荐使用的频段内,达到实用化要求.     

H_∞-based fault detection for nonlinear networked systems with random packet dropout and probabilistic interval delay

The fault detection problem for the nonlinear networked control system(NCS) with packet dropout and delay is investigated.A nonlinear stochastic system model is proposed to account for the NCS with random packet dropout and networkinduced non-uniformly distributed time-varying delay in both from sensor to controller(S/C) and from controller to actuator(C/A).Based on the obtained NCS model,employing an observer-based fault detection filter as the residual generator,the addressed fault detection problem is converted into an auxiliary nonlinear H∞ control problem.Then,with the help of Lyapunov functional approach,a sufficient condition for the desired fault detection filter is constructed in terms of certain linear matrix inequalities,which depend on not only the delay interval but also the delay interval occurrence rate and successful packet communication rate.Especially,a trade-off phenomenon between the maximum allowable delay bound and successful data packet transmission rate is found,which is typically resulted from the limited bandwidth of communication networks.The effectiveness of the proposed method is demonstrated by a simulation example.     

Ho∞-based fault detection for nonlinear networked systems with random packet dropout and probabilistic interval delay

The fault detection problem for the nonlinear networked control system (NCS) with packet dropout and delay is investigated.A nonlinear stochastic system model is proposed to account for the NCS with random packet dropout and networkinduced non-uniformly distributed time-varying delay in both from sensor to controller (S/C) and from controller to actuator (C/A).Based on the obtained NCS model,employing an observer-based fault detection filter as the residual generator,the addressed fault detection problem is converted into an auxiliary nonlinear H∞ control problem.Then,with the help of Lyapunov functional approach,a sufficient condition for the desired fault detection filter is constructed in terms of certain linear matrix inequalities,which depend on not only the delay interval but also the delay interval occurrence rate and successful packet communication rate.Especially,a trade-off phenomenon between the maximum allowable delay bound and successful data packet transmission rate is found,which is typically resulted from the limited bandwidth of communication networks.The effectiveness of the proposed method is demonstrated by a simulation example.     

Improved spectrum sharing algorithm based on feedback control information in cognitive radio networks

In order to avoid the system performance deterioration caused by the wireless fading channel and imperfect channel estimation in cognitive radio networks, the spectrum sharing problem with the consideration of feedback control information from the primary user is analyzed. An improved spectrum sharing algorithm based on the combination of the feedback control information and the optimization algorithm is proposed. The relaxation method is used to achieve the approximate spectrum sharing model, and the spectrum sharing strategy that satisfies the individual outage probability constraints can be obtained iteratively with the observed outage probability. Simulation results show that the proposed spectrum sharing algorithm can achieve the spectrum sharing strategy that satisfies the outage probability constraints and reduce the average outage probability without causing maximum transmission rate reduction of the secondary user.     

微蜂窝MIMO通信系统信道模型研究

城市微蜂窝移动通信电波传播环境相当复杂,特别是针对未来陆地蜂窝移动通信系统基于智能天线应用的规范标准要求,建立这种无线传播信道的有效模型无论对理论分析、系统优化设计以及网络工程规划来说都是非常重要的。为此从多径色散信道的特性分析出发,采用电磁散射理论和电波传播原理建立了基于阵列天线的微蜂窝多输入多输出物理信道模型,对刻画信道特性的一些重要性能参数,如空间相关性和时间演化等进行了仿真分析和研究。与有关文献结果的比较,表明了所建立的微蜂窝移动通信信道模型的有效性。     

战术Ad Hoc网络模拟器的设计与实现

为了克服现有模拟器可扩展性差或只能模拟某一层协议的缺点,设计并开发了战术Ad Hoc网络模拟器,提供无线分组网模拟环境,模拟实际无线分组网信道数据传输特性,为Ad Hoc网络上层协议或算法提供一个不涉及具体无线组网协议、可扩展性好的调试平台,并测试了该模拟器的功能和性能,结果表明该模拟器功能正确、模拟精度高。     

基于网络监视器的网络实验设计

本文介绍了网络监视器的基本功能,提供了用网络监视器设计网络实验的基本方法及已开设的网络实验项目,并通过一个具体实例分析实验结果.     

基于共空间模式和K近邻分类器的脑-机接口信号分类方法

脑-机接口是指在人脑和计算机之间建立的直接的交流和控制通道,它以脑电信号的形式反映人的意识,并转换成控制信号.针对两类运动想象脑电信号的分类问题,提出共空间模式和小波包分解相结合的脑电信号特征提取方法.利用不同小波包对训练集的多路脑电信号进行分解,再用共空间模式算法对不同分解层子带的脑电信号进行特征提取,并采用K近邻分类器对提取到的不同特征进行分类,得到最优小波包函数和小波包子带参数.将结果应用于测试集数据的分类.仿真实验结果表明,选择db4小波包函数和4层小波包分解层,对8个特征点进行分类,可以得到高达96%的正确率.