扩展卡尔曼滤波和不敏卡尔曼滤波在实时雷达回波反演大气波导中的应用

为了改善雷达回波反演大气波导(RFC)方面存在的单时次、单方位角反演的问题,提出利用扩展卡尔曼滤波和不敏卡尔曼滤波的反演算法对大气波导结构的多方位角实时跟踪反演. 在卡尔曼滤波方法中分别给出大气波导结构的参数化方程、观测方程、滤波算法的状态转移方程,最后导出滤波反演算法的迭代求解流程. 在大气波导结构不随时间变化和随时间变化的两种条件下,对扩展卡尔曼滤波和不敏卡尔曼滤波算法进行数值实验. 实验结果表明,不敏卡尔曼滤波更适用于RFC这高度非线性反演问题,它可能今后为大气波导结构多方位角实时跟踪反演的业务化运行提供理论基础与技术保证. 关键词： 大气波导 雷达回波 扩展卡尔曼滤波 不敏卡尔曼滤波     

Sampling strong tracking nonlinear unscented Kalman filter and its application in eye tracking

The unscented Kalman filter is a developed well-known method for nonlinear motion estimation and tracking. However, the standard unscented Kalman filter has the inherent drawbacks, such as numerical instability and much more time spent on calculation in practical applications. In this paper, we present a novel sampling strong tracking nonlinear unscented Kalman filter, aiming to overcome the difficulty in nonlinear eye tracking. In the above proposed filter, the simplified unscented transform sampling strategy with n+2 sigma points leads to the computational efficiency, and suboptimal fading factor of strong tracking filtering is introduced to improve robustness and accuracy of eye tracking. Compared with the related unscented Kalman filter for eye tracking, the proposed filter has potential advantages in robustness, convergence speed, and tracking accuracy. The final experimental results show the validity of our method for eye tracking under realistic conditions.     

神经群模型中癫痫状棘波的闭环控制性能研究

神经群模型可模拟产生癫痫发作间歇期、发作前期和发作期的脑电信号.本文基于代数估计法,给出一种新型的闭环反馈控制策略以消除神经群模型中的癫痫状棘波.代数估计法用以观测模型中的状态以进一步构造控制器.在多个神经群耦合的模型中,通过数值仿真研究了与所给的闭环反馈控制策略相关的一些特性,包括受控神经群的类型与消除棘波的能力之间的关系、受控神经群的数目与控制能量之间的关系、模型的参量和控制能量之间的关系,以期建立合适的控制规则实现利用尽可能小的控制能量消除癫痫状棘波.此外,通过数值仿真对基于代数估计法的闭环反馈控制策略和直接比例反馈控制策略进行比较,结果表明,利用代数估计法进行滤波能减少消除癫痫状棘波所需的控制能量.     

基于UKF的多用户混沌通信

为克服信道噪声、系统参数误配及多用户干扰对混沌通信系统的影响,本文组合不同的状态空间模型并结合盲提取算法,提出了一种双无先导卡尔曼滤波器 (dual unscented Kalman filter, DUKF),以实现多用户的混沌通信.仿真结果表明,在多输入多输出信道的多用户通信环境下,该算法有较快的收敛速度,并能有效地实现多用户的混沌通信. 关键词： 混沌通信 多输入多输出 双无先导卡尔曼滤波器 盲提取     

Reply to “Comment on ‘Ensemble Kalman filter with the unscented transform”’

This is a reply to the comment of Dr. Sakov on the work “Ensemble Kalman filter with the unscented transform” of Luo and Moroz (2009) [2].     

Comment on “Ensemble Kalman filter with the unscented transform”

The results of numerical experiments with the ensemble unscented Kalman filter and 40-dimensional model of Lorentz and Emanuel in Luo and Moroz (2009) [2] are inconclusive.     

Robust tracking of moving sound source using scaled unscented particle filter

Tracking an active sound source involves the modeling of non-linear non-Gaussian systems. To address this problem, this paper proposed scaled unscented particle filter (SUPF) algorithm for tracking moving sound source. The particle filter part of the SUPF provides the general probabilistic framework to handle non-linear non-Gaussian systems, and the scaled unscented Kalman filter (SUKF) part of the SUPF generates better proposal distributions by taking into account the most recent observation. Meanwhile, models used in SUPF algorithm were also explored for the sound source motion, observation and the likelihood of the sound source location in the light of the Langevin process. Compared with the conventional PF approach, the simulated results demonstrated that the SUPF algorithm had superior tracking performance.     

基于不敏粒子滤波的水中非合作磁性目标实时磁定位方法

针对水中非合作磁性目标的实时定位问题, 提出了一种基于不敏粒子滤波(unscented particle filter, UPF)的实时磁定位方法. 从非合作磁性目标的运动特征出发, 建立了状态空间模型, 利用UPF算法对目标状态进行实时估计. 为了提高系统的可观测性, 在算法迭代过程中对粒子状态进行约束及利用最小二乘法反演磁矩. 仿真与铁磁物体定位实验结果表明, 该方法的定位精度较高, 实时定位效果较好, 可用于近场实时磁定位问题中. 关键词： 磁定位 椭球体模型 状态空间模型 不敏粒子滤波     

A Kalman filter algorithm based on exact modeling for FOG GPS/SINS integration

The Kalman filter is widely applied in fiber optic gyro (FOG) inertial integrated navigation system. To solve the problem of hard acquirement of Kalman filter parameters, a novel algorithm for FOG GPS/SINS integration navigation based on exact modeling is proposed in this paper. The models of inertial sensors using Allan variance analysis are established in proposed algorithm and the precise Kalman filter model is obtained based on the correspondence between Allan variance coefficients and inertial sensors parameters. The simulation and experimental results show that Kalman filter parameters can be obtained for GPS/SINS integrated navigation system precisely and efficiently based on Allan variance modeling method, and the algorithm has reference value for theoretical perfection and engineering applications.     

基于广义M估计的鲁棒容积卡尔曼滤波目标跟踪算法

为减小测量异常误差对非线性目标跟踪系统的影响, 提出了一种基于广义M估计的鲁棒容积卡尔曼滤波算法. 首先将非线性测量方程等价变换, 利用约束总体最小二乘准则构建广义M估计极值函数, 在不进行线性化近似的前提下将其引入到容积卡尔曼滤波求解框架中. 然后根据Mahalanobis距离构建异常误差判别量, 利用卡方分布的置信水平确定判决门限, 并建立改进的三段Huber权函数, 使其能够降低小异常误差权值, 剔除大异常误差. 理论分析表明, 该方法具有无需求导、跟踪精度高、实时性好等优点, 且无需已知异常误差的统计特性; 实验结果表明, 所提算法能够有效减小异常误差的影响, 在实际非线性物理系统中具有广阔的应用空间.     

基于卡尔曼滤波的分布式光纤Raman测温系统

分布式光纤喇曼测温系统采用了线性逼近的方法进行温度解调,并通过灵敏度分析法获取了相应的线性解调参数。为了提高系统的精度,在信号累加平均去噪的基础上,采用了卡尔曼滤波的方法,通过对前一时刻的估计值与当前测量值的递推,实现了对存在测量噪声的温度信号的最优化估计,完成了对现场采集信号的实时更新和处理。实验验证了该滤波方案的可行性,随着外界环境温度的变化,测温系统能够得到合理的温度曲线,测量误差小于1℃。系统响应性良好,性能稳定,能够适应复杂的环境变化。     

Pose estimation for UAV aerial refueling with serious turbulences based on extended Kalman filter

In recent years, many pose estimation algorithms were developed, and have been successfully applied to solve unmanned aerial vehicle (UAV) aerial refueling pose estimation problems. This paper mainly focuses on solving this problem under serious turbulences circumstance. The extended Kalman filter is a set of mathematical equations to estimate the state of a process, which is able to support estimations of past, present, and even future states. In reference to previous papers and some simulations, we build up the noise models of refueling boom and atmospheric turbulence. Then, an extend Kalman filter is adopted to solve the pose estimation problem in UAV aerial refueling with serious turbulences. The experimental results demonstrate the feasibility and effectiveness of our proposed approach.     

机载光电系统中光纤陀螺卡尔曼滤波方法应用

针对光纤陀螺输出噪声大、随机漂移难抑制,从而影响机载光电系统低频性能的问题,分析了系统中光纤陀螺输出的实测数据,根据其噪声特性建立了光纤陀螺噪声的AR2 模型。研究了基于卡尔曼滤波的光纤陀螺在机载光电系统中的应用方法。对比传统应用的滑动平均滤波,给出实验结果。结果表明:卡尔曼滤波能有效消除陀螺高频噪声,抑制系统的随机漂移,在一定程度上能够提高系统的低频工作性能。研究结果对机载系统中光纤陀螺的应用有较好的参考价值。     

基于时频差的正交容积卡尔曼滤波跟踪算法

针对基于时频差测量的无源跟踪中面临的非线性估计问题, 提出一种正交容积卡尔曼滤波跟踪算法. 该算法在容积卡尔曼滤波算法的基础上, 通过引入特定正交矩阵改进容积采样方法, 在高维状态估计下减小因采样产生的误差, 在没有增加计算量的前提下, 有效提高收敛速度及跟踪精度. 仿真结果表明, 在基于到达时差和到达频差的联合无源跟踪问题中, 与扩展卡尔曼滤波及容积卡尔曼滤波算法相比, 本文所提算法在跟踪性能上有明显提升.     

Robust tracking of moving sound source using multiple model Kalman filter

In this paper a novel method for tracking an active speaker in a noisy and reverberant environment by means of a spatially distributed microphone array is presented. Firstly, a sound source localization algorithm based on time delays of arrival (TDOA) in microphone pairs provides observed position estimates. Then these remarkably noisy estimates are filtered by a multiple model Kalman filter (MMKF) in order to obtain a smoothed trajectory of the speaker’s movement. Compared with the traditional Kalman filter (KF), simulated results prove the MMKF is more robust and effective in noisy environments.     

二级Kalman滤波的被动声定位后置处理方法

阵列定位的计算结果中往往存在大量的野值,需要用后置处理方法从结果中选取真实目标信息。本文提出了一种基于Kalman滤波的二级后置处理方法,建立了相应的滤波模型,并对主要参数进行讨论选取,同时给出了外场实验结果。结果表明此方法后置处理效果明显,并可以满足系统实时性的要求。     

Control of fractional chaotic system based on fractional-order resistor—capacitor filter

We present a new fractional-order resistor-capacitor controller and a novel control method based on the fractional-order controller to control an arbitrary three-dimensional fractional chaotic system. The proposed control method is simple, robust, and theoretically rigorous, and its anti-noise performance is satisfactory. Numerical simulations are given for several fractional chaotic systems to verify the effectiveness and the universality of the proposed control method.     

基于DSP的电动汽车锂电池荷电状态估算的研究与实现

锂电池荷电状态(SOC)的准确估算是电动汽车能源管理的关键技术。为了提高锂电池SOC的估算精度,将无迹卡尔曼滤波(UKF)应用于锂电池SOC估算,以减小拓展卡尔曼滤波(EKF)简单线性化带来的误差。搭建电池检测系统的硬件平台,以TMS320F28335型数字信号处理器(DSP)为主控芯片(MCU),实现电压、电流、温度的检测及UKF算法,并设计了相关的电池测试实验。实验结果表明,UKF可以实时估算锂电池SOC,估算误差在4%以内,高于传统的拓展卡尔曼滤波(EKF)。     

基于Volterra滤波器的参量阵扬声器互调失真补偿研究

针对单边带调制方法预处理的参量阵扬声器系统在复杂信号输入情况下存在明显互调失真的问题,引入Volterra滤波器模型和简化的一维Volterra滤波器模拟了其互调失真。设计了相应的逆滤波器,并对其抑制系统互调失真的性能进行了实验对比。结果表明,所设计的逆滤波器都能有效降低系统的互调失真,而后者具有较低的计算复杂度。     

基于Washout滤波器技术的Colpitts振荡器混沌控制研究

研究了如何利用Washout滤波器的混沌控制技术来控制Colpitts振荡器中的混沌.首先得到振荡电路的归一化状态方程及基于Washout滤波器技术的控制方程，然后通过调整滤波器的控制参数，将混沌状态的Colpitts振荡器控制到稳定平衡点或周期振荡状态，最后分析了控制结果，进行了数值仿真，验证了该控制方法的有效性.