混合退火粒子滤波器

针对非线性、非高斯系统状态的在线估计问题，提出一种新的基于序贯重要性抽样的粒子滤波算法. 在滤波算法中，用状态参数分解和退火系数来产生重要性概率密度函数，此概率密度函数综合考虑了转移先验、似然、噪声的统计特性以及最新的观察数据，因此更接近于系统状态的后验概率. 理论分析与仿真实验表明该粒子滤波器的性能明显优于标准的粒子滤波器和扩展卡尔曼滤波器. 关键词： 非线性 非高斯 粒子滤波 序贯重要性抽样     

基于混合粒子滤波的水下小目标跟踪

针对水下小目标粒子滤波估计过程中“粒子贫化”引起的估计性能下降,提出了混合粒子滤波算法。该算法在常规粒子滤波算法基础上,在每一步迭代估计过程中进行量测的再次随机采样,以丰富随机粒子多样性,缓解水下小目标状态估计过程中的“粒子贫化”的影响。对算法进行了仿真分析,并将该方法用于水下小目标探测实验的数据处理,结果表明,相比于常规的粒子滤波算法,所提出的混合粒子滤波得到了误差更小且稳定的状态估计结果,有效地改善水下小目标跟踪的精度和稳健性。     

自调整平滑区间粒子滤波平滑算法

针对非线性系统的状态估计问题, 提出了一种自调整平滑区间粒子滤波平滑算法. 该算法的显著特点是根据采样粒子观测值与系统状态观测值之间的偏差动态修正滤波平滑区间的长度, 有效抑制了传统的粒子滤波平滑算法中因区间长度固定可能造成粒子权重重新赋值带来误差增大的问题. 该算法的原理是依据粒子滤波器的工作机理, 把系统状态信息和热槽组成一个抽象的整体, 将粒子滤波平滑过程类比为观测信息和热槽交互的统计力学系统. 在无新的观测信息时, 整个系统遵循热力学第二定律, 即无论从任何初始状态出发, 整个力学系统的熵是非减的; 而当出现新的观测信息时, 粒子滤波器像麦克斯韦妖从新的观测信息中抽取能量传送给热槽, 使整个抽象系统的熵减少, 根据系统熵的递变规律变化对滤波平滑区间的长度加以动态修正, 优化粒子的权重赋值, 进而提高系统状态的估计精度. 利用matlab进行了仿真分析, 验证了该算法的有效性.     

State of charge estimation of Li-ion batteries in an electric vehicle based on a radial-basis-function neural network

The on-line estimation of the state of charge(SOC) of the batteries is important for the reliable running of the pure electric vehicle in practice.Because a nonlinear feature exists in the batteries and the radial-basis-function neural network(RBF NN) has good characteristics to solve the nonlinear problem,a practical method for the SOC estimation of batteries based on the RBF NN with a small number of input variables and a simplified structure is proposed.Firstly,in this paper,the model of on-line SOC estimation with the RBF NN is set.Secondly,four important factors for estimating the SOC are confirmed based on the contribution analysis method,which simplifies the input variables of the RBF NN and enhances the real-time performance of estimation.Finally,the pure electric buses with LiFePO 4 Li-ion batteries running during the period of the 2010 Shanghai World Expo are considered as the experimental object.The performance of the SOC estimation is validated and evaluated by the battery data from the electric vehicle.     

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.     

基于AGPF的滚动轴承性能衰退趋势预测

针对粒子滤波算法中粒子退化和计算复杂度问题,提出了一种自适应遗传粒子滤波(AGPF)算法。该算法采用遗传算法代替传统粒子滤波中的重采样方法,并根据粒子数与滤波误差方差之间的关系,自适应调节滤波过程中的粒子数。通过预测滚动轴承的性能衰退趋势,对该方法进行验证,结果表明,AGPF算法能够在保证预测精度的条件下,减少滤波粒子数,更加适用于滚动轴承的性能衰退趋势预测。     

基于Stiefel流形的粒子滤波器研究

为了解决粒子滤波的粒子退化和粒子多样性丧失问题,提出了一种基于Stiefel流形的粒子滤波算法.该算法将系统模型置于Stiefel流形上,用朗之万分布描述过程转移概率分布,用矩阵正态分布表示似然函数分布,在流形分布上进行粒子采样.在计算加权粒子的均值时,将流形嵌入到欧氏空间中,先计算欧氏空间中的粒子均值,再将计算结果投影到嵌套流形上,这就排除了噪声统计特性对粒子权重方差的影响,得到了一种受系统状态模型限制较少的重要性概率密度函数通用选择方案.仿真时选取单变量非静态增长模型,仿真结果验证了该算法的实时性、鲁棒性,滤波精度和滤波效率均比无味粒子滤波算法更好.     

光度曲线反演空间目标形状的发展现状

光度曲线反演空间目标形状和姿态等参数为空间目标监视与识别提供了新的技术途径。首先分析了基于光度曲线反演空间目标信息的必要性,之后通过关注和跟踪国内外最新发展情况,全面总结了目前进行形状反演的3种主流方法的研究现状及优缺点,对3种反演方法进行了比较分析,指出基于非线性滤波技术的形状反演方法是未来的主要发展方向,并分析了3种非线性滤波技术的优缺点。最后针对反演存在的问题,指出了基于改进的实时性更好的粒子滤波算法,融合多种传感器信息反演几何形状和表面材料更为复杂的空间目标是下一步研究发展的主要方向。     

利用粒子滤波从雷达回波实时跟踪反演大气波导

粒子滤波(particle filter,PF)是利用蒙特卡洛仿真方法处理递推估计问题的非线性滤波算法,这种方法不受模型线性和高斯假设的约束,是处理非线性非高斯动态系统状态估计的有效算法,适用于雷达回波反演大气波导(RFC)这类非线性非高斯问题.文中分别介绍了PF的基本思想和具体算法实现步骤,最后导出PF反演算法的迭代求解格式.数值试验结果表明,与扩展卡尔曼滤波(extended kalman filter,EKF)和不敏卡尔曼滤波(unscented kalman filter,UKF)相比,PF更适用于RFC这类高度非线性反演问题,可有效提高反演结果的稳定性和精度.     

基于Huber的高阶容积卡尔曼跟踪算法

为改善高阶容积卡尔曼滤波算法的滤波精度和鲁棒性, 提出了一种新的基于Huber的高阶容积卡尔曼滤波算法. 在采用统计线性回归模型近似非线性量测模型的基础上, 利用Huber M 估计算法实现状态的量测更新. 进一步结合高阶球面-径向容积准则的状态预测模块构成基于 Huber的高阶容积卡尔曼跟踪算法. 重点分析了Huber代价函数的调节因子对算法跟踪性能的影响. 通过对纯方位目标跟踪和再入飞行器跟踪两个实例验证了所提算法的跟踪性能优于传统高阶容积卡尔曼滤波算法.     

光纤水听器相位生成载波解调非线性因素的抑制方法

为了消除非线性因素对相位生成载波解调结果的干扰,降低相位生成载波解调过程中低通滤波器的频响特性对相位生成载波解调结果的影响,该文提出了一种基于扩展卡尔曼滤波椭圆参数估计的相位生成载波微分交叉相乘解调方法。该方法在综合考虑各种非线性因素对相位生成载波微分交叉相乘解调结果的影响的基础上,对传统的相位生成载波微分交叉相乘解调过程进行了改进。经过计算机仿真和实验验证,该文所提出的扩展卡尔曼滤波微分交叉相乘方法能够有效地抑制非线性因素对相位生成载波解调结果的干扰,相比传统的相位生成载波微分交叉相乘方法,其信噪比提高了35.0 d B,总谐波失真降低了30.7 dB,信噪谐波比提高了31.0 dB,且扩展卡尔曼滤波微分交叉相乘方法受解调过程中低通滤波器频响特性的影响较小。     

Tracking noisy limit cycle oscillation with nonlinear filters

For engineering systems, the dynamic state estimates provide valuable information for the detection and prediction of failure due to noise and vibration. From this perspective, nonlinear filtering techniques are applied to the problem of state estimation of dynamical systems undergoing noisy limit cycle oscillation. Specifically, the extended Kalman filter, ensemble Kalman filter and particle filter are used to track the limit cycle oscillations of a Duffing oscillator using noisy observational data. The noisy limit cycle oscillations feature highly non-Gaussian trends. The efficiency and limitations of the extended Kalman filter, ensemble Kalman filter and particle filter in tracking limit cycle oscillations are examined with respect to the model and measurement noise and sparsity of measurement data. For the limit cycle oscillations considered here, it is demonstrated that the ensemble Kalman filter and particle filter outperform the extended Kalman filter in the presence of sparse observational data or strong measurement noise. For moderate measurement noise and frequent measurement data, the ensemble Kalman filter and particle filter perform equally well in comparison to the extended Kalman filter.     

区间衍生粒子滤波器

针对非线性、非高斯环境下具有不确定动态模型参数的系统状态估计问题,提出了一种新颖的区间衍生粒子滤波算法.该算法利用区间滤波生成的重要性密度函数,在系统状态转移概率密度的基础上,融入最新的系统观测数据,从而提高了对系统状态后验概率的逼近程度.为了进一步提高算法的实时性,提出一种类似光子衍射的粒子衍生过程,进而缓解了滤波精度与运算量之间的矛盾.通过陀螺/星敏感器组合定姿问题验证了该算法的有效性和鲁棒性.     

An integrated toll and ramp control methodology for dynamic freeway congestion management

This paper investigates an integrated freeway traffic management system, which coordinates both dynamic toll pricing and ramp control strategies for the purpose of dynamic freeway congestion management. The proposed integrated dynamic toll-ramp control methodology is built mainly on the principles of stochastic optimal control approaches, involving two developmental procedures. First, through detector configurations and system specification, a discrete-time nonlinear stochastic system is formulated to characterize the time-varying relationships of system states, control variables, and traffic data. Then, by employing the extended Kalman filtering technology, a stochastic optimal control based algorithm is proposed to execute the integrated dynamic toll and ramp control mechanism. With the aid of the Paramics microscopic traffic simulator, numerical studies under various simulated freeway congestion scenarios are conducted. Corresponding numerical results demonstrate the applicability of the proposed methodology in response to diverse freeway traffic congestion phenomena, and its relative advantages in improving both the average travel time and hourly throughputs by 16.4% and 16.5%, respectively.     

A Nonlinear Maximum Correntropy Information Filter for High-Dimensional Neural Decoding

Neural signal decoding is a critical technology in brain machine interface (BMI) to interpret movement intention from multi-neural activity collected from paralyzed patients. As a commonly-used decoding algorithm, the Kalman filter is often applied to derive the movement states from high-dimensional neural firing observation. However, its performance is limited and less effective for noisy nonlinear neural systems with high-dimensional measurements. In this paper, we propose a nonlinear maximum correntropy information filter, aiming at better state estimation in the filtering process for a noisy high-dimensional measurement system. We reconstruct the measurement model between the high-dimensional measurements and low-dimensional states using the neural network, and derive the state estimation using the correntropy criterion to cope with the non-Gaussian noise and eliminate large initial uncertainty. Moreover, analyses of convergence and robustness are given. The effectiveness of the proposed algorithm is evaluated by applying it on multiple segments of neural spiking data from two rats to interpret the movement states when the subjects perform a two-lever discrimination task. Our results demonstrate better and more robust state estimation performance when compared with other filters.     

改进粒子滤波在汽轮机故障诊断中的应用夏飞,郝硕涛

针对汽轮机的振动信号容易受到较为复杂的随机噪声污染,提出了一种改进粒子滤波的振动信号降噪方法。首先建立采集振动信号的数学模型,将其作为粒子滤波的状态方程;然后利用小波分析提取采集振动信号的背景噪声,将其和状态信号一起作为观测信号,得到观测方程,把降噪问题转化成在状态空间模型下的滤波问题。由于采用序贯重要性采样的粒子滤波存在着样本退化问题,在重采样阶段采用了一种权值排序、优胜劣汰的重采样算法,就是对各粒子的归一化权值从小到大的排列顺序,并根据权值方差大小淘汰粒子,从而得到了改进的粒子滤波算法,在一定程度上解决了标准粒子滤波的退化问题。进而运用改进粒子滤波算法对振动信号进行降噪处理,降噪前信号和降噪后信号分别通过小波包分解系数求取频带能量,根据各个频带能量的变化提取故障特征向量浓缩了汽轮机振动故障的全部信息,对提取的故障特征向量应用诊断识别算法进行故障模式识别。通过对比降噪前信号和降噪后信号的故障诊断识别率,证明了改进粒子滤波在汽轮机故障诊断中的应用效果更佳。     

Dim moving target tracking algorithm based on particle discriminative sparse representation

The small dim moving target usually submerged in strong noise, and its motion observability is debased by numerous false alarms for low signal-to-noise ratio (SNR). A target tracking algorithm based on particle filter and discriminative sparse representation is proposed in this paper to cope with the uncertainty of dim moving target tracking. The weight of every particle is the crucial factor to ensuring the accuracy of dim target tracking for particle filter (PF) that can achieve excellent performance even under the situation of non-linear and non-Gaussian motion. In discriminative over-complete dictionary constructed according to image sequence, the target dictionary describes target signal and the background dictionary embeds background clutter. The difference between target particle and background particle is enhanced to a great extent, and the weight of every particle is then measured by means of the residual after reconstruction using the prescribed number of target atoms and their corresponding coefficients. The movement state of dim moving target is then estimated and finally tracked by these weighted particles. Meanwhile, the subspace of over-complete dictionary is updated online by the stochastic estimation algorithm. Some experiments are induced and the experimental results show the proposed algorithm could improve the performance of moving target tracking by enhancing the consistency between the posteriori probability distribution and the moving target state.     

粒子区别性稀疏表征的小弱运动目标跟踪算法

评估每个粒子的重要性是确保粒子滤波法跟踪目标准确性的重要因素。针对背景杂波和噪声干扰形成的大量虚警导致小弱目标跟踪识别的随机性和不确定性问题, 提出了一种基于粒子区别性稀疏表征的小弱目标跟踪方法。该方法根据红外图像信号自适应构建分类超完备字典, 即反映目标信号特征的目标字典和表示背景杂波的背景字典, 有利于突出目标粒子和背景粒子在联合分类字典的稀疏表征差异程度;建立基于目标粒子和背景粒子稀疏重构残差差异性的粒子滤波观测模型, 采用随机估计法对字典子空间进行在线更新, 实现对目标状态估计与跟踪。理论分析和试验结果表明, 该方法增强了随机粒子的状态估计能力, 提升了粒子稀疏表征对小弱运动目标的适应能力和跟踪识别准确度。     

基于粒子滤波的运动目标光电定位仿真研究

运动目标的光电定位不能像静止目标那样简单做均值滤波，鉴于此，引入粒子滤波算法，它不仅可以应用于线性系统，而且还适用于非线性系统。结合光电定位需求，详细推导了计算公式及初值和参数选取公式，对只含测量噪声以及含有测量和运动噪声等的海面运动目标光电无源定位算法进行了仿真计算，验证了算法的有效性，讨论了噪声强度对滤波效果的影响，滤波参数选择对滤波效果的影响，目标运动方式对滤波跟随性的影响，重采样算法对滤波效果的影响等。所得结论为:粒子滤波可用于运动目标光电定位过程，可有效降低定位误差；粒子滤波算法具有较强鲁棒性，适用于噪声较大、目标运动形态变化大等情况。     

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