基于Sh相关系数的非线性系统状态粒子估计算法

本文提出一种适用于非线性系统状态的粒子估计算法--基于Sh相关系数的粒子估计(PE)算法.该算法主要由预测、更新和平滑组成,利用被估计状态观测值路径和粒子观测值路径之间的Sh相关系数来修正粒子权值.仿真实验结果表明,该算法在状态估计精度上优于序列重要性重采样(SIR)算法、辅助粒子滤波(APF)算法、正则化粒子滤波(RPF)算法、高斯粒子滤波(GPF)算法和高斯混合粒子滤波(GSPF)算法.     

基于IMMEPF的多普勒盲区目标异类多传感器联合跟踪

对于机载脉冲多普勒雷达,多普勒盲区是不可避免的。为解决多普勒盲区内机动目标跟踪问题,提出了基于扩展卡尔曼粒子滤波(IMMEPF)的雷达和ESM联合跟踪算法。该算法融合了交互式多模型(IMM)、粒子滤波(PF)和扩展卡尔曼滤波(EKF)的优势,采用多模型结构以匹配目标的运动模型。粒子滤波能处理非线性、非高斯问题,而采用EKF产生粒子,由于考虑了当前观测值,使得粒子的分布更接近后验概率密度分布,克服粒子的退化现象,从而提高估计精度。仿真结果表明,给出的算法能够显著提高对落入多普勒盲区内的目标点迹的跟踪精度。     

粒子滤波和正则粒子滤波算法性能研究

主要介绍了解决系统状态估计问题的滤波算法。在提出非线性高斯系统模型的基础上着重阐述了扩展卡尔曼滤波(EKF)、粒子滤波(PF)和正则粒子滤波(RPF)算法。对这三种算法在不同的噪声条件下的估计性能进行了仿真分析。结果表明,在非线性高斯系统中,PF和RPF的估计性能远比EKF的估计性能要好,由于RPF是从离散分布中重构其近似连续分布,再从该连续分布中采样粒子,估计性能比PF要好,尤其在小噪声的环境下,估计性能更加稳定。     

基于ISODATA聚类优化的粒子滤波算法

针对粒子滤波(PF)中存在粒子贫化的现象,将ISODATA聚类优化思想引入粒子滤波中.该算法选择相似性度量和聚类准则函数,使用迭代算法,逐步优化聚类结果,使准则函数达到极值,即使粒子集可以向后验概率密度分布取值较大的区域运动.通过对粒子集的优化,克服了粒子贫化的问题.实验结果表明,该算法具有较高的预估精度和较好的鲁棒性.     

粒子优化组合的粒子滤波跟踪算法研究

为了改善运动目标跟踪问题中粒子滤波算法(PF)的估计精度,针对粒子滤波出现样本退化以及样本贫乏问题,提出了一种基于粒子优化组合的粒子滤波算法(POCPF)。该算法用一能使粒子朝后验概率分布值较大的区域运动的似然函数来改善粒子的位置,然后重估粒子的权值对粒子进行优化组合,满足粒子的多样性要求,从而克服了粒子的贫乏问题,在一定程度上减少系统状态为达到精确度所需的粒子数目,缩短运行时间。通过仿真实验证明了POCPF算法的有效性,当粒子数相同时,POCPF算法性能优于PF算法。     

基于最优采样函数的粒子滤波算法与贝叶斯估计

传统粒子滤波器(PF)直接根据状态演化方程产生新的粒子,由于没有考虑新近观测对状态估计的影响,这种滤波器性能较差,即便在粒子数目很大的情况也是如此。为此,本文提出一种基于序贯重要采样(SIS)的改进粒子滤波算法,该算法采用集成了新近观测量的最优采样(或重要密度)函数指导粒子的生成,使粒子权值的方差最小化,能有效减轻粒子退化问题;同时。在粒子重采样之后增加了马尔科夫链蒙特卡洛(MCMC)过程,消除了重采样引起的粒子贫化的负面影响,从而使粒子的多样性得以保持。对非线性系统的状态估计和只测角跟踪的仿真实例均表明,本文所提出的算法比传统估计算法如EKF,UKF具有更高的精度和更强的鲁棒性;与标准PF相比,其性能也有较大的提高,并可以在相同的估计精度下大大减少所需的粒子数目,是一种有效的非线性滤波算法。     

基于改进粒子滤波的鲁棒目标跟踪算法

为了克服样本贫化现象导致的滤波发散,本文对重采样后的粒子进行有方向性的变异操作,在增加样本集的多样性同时使粒子集更集中均匀的分布在目标的邻域.同时把Mean Shift算法引入粒子滤波(PF)框架中,对PF估计结果迭代得到最优的目标状态,并用迭代得到的状态值控制粒子变异的方向.仿真实验表明,本文提出的方法具有更高的估计...     

毫米波/红外多传感器融合跟踪算法研究

毫米波/红外(MMW/IR)传感器是各国发展多模复合制导技术的重点.针对平方根无迹卡尔曼滤波(SR-UKF)的估计算法存在线性化误差及粒子滤波中得到优化的重要性密度函数比较困难的问题,将平方根无迹卡尔曼滤波与粒子滤波相结合,提出一种序贯融合的平方根无迹卡尔曼粒子滤波(SRUKPF)算法.利用平方根无迹卡尔曼算法得到的状态更新矩阵和误差协方差矩阵,构造粒子滤波的重要性密度函数,这样重要性密度函数能够融入最新观测信息,进而更加符合真实状态的后验概率分布.为验证算法的有效性,以地空导弹中MMW/IR传感器复合制导为背景进行仿真研究与分析,结果表明,该算法克服了粒子滤波法难以得到优化重要性密度函数的缺陷,能有效提高多传感器系统状态估计的精度     

一种基于差分演化的粒子滤波算法

针对粒子滤波(Particle Filter, PF)存在的粒子退化和贫化问题,该文提出一种基于差分演化(Differential Evolution, DE)的PF算法。首先,为了充分利用最新的观测信息,采用无迹卡尔曼滤波(Unscented Kalman Filter, UKF)来产生重要性分布,对重要性分布产生的采样粒子不再做传统重采样操作,而是直接把采样粒子当作DE中的种群样本,粒子权重作为样本的适应函数,对粒子做差分变异、交叉、选择等迭代优化,最后得到最优的粒子点集。试验结果表明,该算法有效缓解了传统PF算法中的粒子退化和贫化,提高了粒子的利用率,具有较好的估计精度。     

基于辅助变量粒子滤波的空对海BO-TMA的研究

论文探讨了TMA(目标运动分析)中基本的非线性估计问题;介绍了粒子滤波(PF)的基本思想和辅助变量PF(AVPF)的基本算法,特别针对空对海单站只测方位TMA(BO-TMA)问题应用AVPF和EKF(扩展卡尔曼滤波)进行了对照研究;建立了问题的离散非线性滤波估计模型;设计了典型的应用场景;给出了Monte Carlo仿真运行结果;表明AVPF具有更高的估计精度、更好的收敛特性和滤波一致性。     

基于遗传粒子滤波的GPS定位数据处理算法

针对粒子滤波算法中的权值退化问题,提出了一种遗传算法辅助下的粒子滤波算法。利用遗传算法在选择继承性上的优势,将遗传算法中的选择、交叉和变异操作引入到粒子滤波算法中。在此基础上,将改进的粒子滤波算法与建立的全球定位系统(GPS)非线性动态状态空间模型结合应用于GPS定位数据处理问题,通过采集实测GPS数据将改进粒子滤波算法与基本粒子滤波算法做了比较。结果表明,遗传算法辅助下的粒子滤波可以增加有效粒子数目,有效解决粒子退化问题,可提高GPS定位数据处理精确度。     

基于迭代扩展Kalman滤波建议分布和线性优化重采样的快速同步定位与构图

针对标准快速同步定位与构图(FastSLAM)方法中由于样本退化及贫化导致自主水下航行器(Autonomous Underwater Vehicle, AUV)及路标位置估计精度严重下降的问题,该文提出一种基于迭代扩展Kalman滤波(Iterative Extended Kalman Filter, IEKF)建议分布和线性优化重采样的FastSLAM方法,通过IEKF融入最新观测值从而降低样本退化,为了降低样本的贫化,将重采样过程中复制的样本与部分被抛弃的样本通过线性组合产生新样本。建立AUV的运动学模型、特征模型及传感器的测量模型,通过Hough变换提取特征构建全局地图,采用改进的FastSLAM方法基于海试数据进行了AUV同步定位与构图试验,结果表明该文所设计的方法能够有效避免样本的退化及贫化,提高了AUV及路标的位置估计精度;此外,一致性分析结果表明所设计算法具有长期一致性。     

杂波环境下的粒子滤波器数据关联方法

为了解决杂波环境下多机动目标的数据关联难题,提出了一种将粒子滤波器(PF)和联合概率数据关联(JPDA)相结合的数据关联算法,该方法首先应用粒子滤波方法对目标的状态进行采样,得到样本(粒子),并结合量测,通过JPDA方法计算得到联合互连事件的关联概率,而该关联概率实际上就是PF中粒子的权值。通过选取适当的有效采样尺度作为衡量PF退化现象的测度,采用重要性重采样技术克服了标准PF的退化现象,降低了算法的计算量。仿真结果表明,粒子滤波方法可以较好地解决杂波环境下跟踪多机动目标的数据关联问题;重要性重采样PF的计算复杂度低于标准PF。     

Robust tracking algorithm for wireless sensor networks based on improved particle filter

Benefitting from its ability to estimate the target state's posterior probability density function (PDF) in complex nonlinear and non‐Gaussian circumstance, particle filter (PF) is widely used to solve the target tracking problem in wireless sensor networks. However, the traditional PF algorithm based on sequential importance sampling with re‐sampling will degenerate if the latest observation appear in the tail of the prior PDF or if the observation likelihood is too peaked in comparison with the prior. In this paper, we propose an improved particle filter which makes full use of the latest observation in constructing the proposal distribution. The quality prediction function is proposed to measure the quality of the particles, and only the high quality particles are selected and used to generate the coarse proposal distribution. Then, a centroid shift vector is calculated based on the coarse proposal distribution, which leads the particles move towards the optimal proposal distribution. Simulation results demonstrate the robustness of the proposed algorithm under the challenging background conditions. Copyright © 2010 John Wiley & Sons, Ltd.     

An improved Markov Chain Monte Carlo method for MIMO iterative detection and decoding

Recently, a new soft-in soft-out detection algorithm based on the Markov Chain Monte Carlo （MCMC） simulation technique for Multiple-Input Multiple-Output （MIMO） systems is proposed, which is shown to perform significantly better than their sphere decoding counterparts with relatively low complexity. However, the MCMC simulator is likely to get trapped in a fixed state when the channel SNR is high, thus lots of repetitive samples are observed and the accuracy of A Posteriori Probability （APP） estimation deteriorates. To solve this problem, an improved version of MCMC simulator, named forced-dispersed MCMC algorithm is proposed. Based on the a posteriori variance of each bit, the Gibbs sampler is monitored. Once the trapped state is detected, the sample is dispersed intentionally according to the a posteriori variance. Extensive simulation shows that, compared with the existing solution, the proposed algorithm enables the markov chain to travel more states, which ensures a near-optimal performance.     

基于改进粒子滤波的GPS接收机自主完好性监测算法

针对基本粒子滤波重采样过程中粒子权值退化和多样性丧失的问题,将遗传算法引入基于神经网络的权值调整粒子滤波算法中,结合了遗传算法全局寻优的收敛性与神经网络局部寻优的快速性优点。将提出的算法与对数似然比方法结合用于GPS接收机自主完好性监测,通过建立一致性检验统计量实现对故障卫星的检测与隔离。通过采集实测数据进行验证,结果表明:该算法可以成功检测和隔离故障卫星,其性能优于基于基本粒子滤波的接收机自主完好性监测,验证了该算法应用于GPS接收机自主完好性监测的可行性和有效性。     

An improved unscented particle filter approach for lithium-ion battery remaining useful life prediction

Lithium-ion rechargeable batteries are widely used as power sources for mobile phones, laptops and electric cars, and gradually extended to military communication, navigation, aviation, aerospace and other fields. Accurate remaining useful life (RUL) prediction of lithium-ion battery plays an important role in avoiding serious security and economic consequences caused by failure to supply required power levels. Thus, the RUL prediction for lithium-ion battery has become a critical task in engineering practices. With its superiority in handling nonlinear and non-Gaussian system behaviors, the particle filtering (PF) technique is widely used in the remaining life prediction. However, the choice of importance function and the degradation of diversity in sampling particles limit the estimation accuracy. This paper presents an improved PF algorithm, that is, the unscented particle filter (UPF) based on linear optimizing combination resampling (U-LOCR-PF) to improve the prediction accuracy. In one aspect, the unscented Kalman filter (UKF) is used to generate a proposal distribution as an importance function for particle filtering. In the other aspect, the linear optimizing combination resampling (LOCR) algorithm is used to overcome the particle diversity deficiency. It should be noted that the step coefficient K can affect the performance of LOCR algorithm, and the fuzzy inference system is applied to determine the value of step coefficient K. According to the analysis results, it can be seen that the proposed prognostic method shows higher accuracy in the RUL prediction of lithium-ion battery, compared with the existing PF-based and UPF-based prognostic methods.     

Direct tracking of noncircular sources for multiple arrays via improved unscented particle filter method

Direct tracking problem of moving noncircular sources for multiple arrays is investigated in this study. Here, we propose an improved unscented particle filter (I-UPF) direct tracking method, which combines system proportional symmetry unscented particle filter and Markov Chain Monte Carlo (MCMC) algorithm. Noncircular sources can extend the dimension of sources matrix, and the direct tracking accuracy is improved. This method uses multiple arrays to receive sources. Firstly, set up a direct tracking model through consecutive time and Doppler information. Subsequently, based on the improved unscented particle filter algorithm, the proposed tracking model is to improve the direct tracking accuracy and reduce computational complexity. Simulation results show that the proposed improved unscented particle filter algorithm for noncircular sources has enhanced tracking accuracy than Markov Chain Monte Carlo unscented particle filter algorithm, Markov Chain Monte Carlo extended Kalman particle filter, and two-step tracking method.     

一种基于混合MCMC的时域波达方向估计算法

基于马尔科夫链蒙特卡洛（Markov Chain Monte Carlo,MCMC）方法的时域波达方向估计算法通过构造马尔科夫链的方式来对波达方向进行估计,但是现有的算法在马尔科夫链的收敛速度和结果上并没有表现出很好的鲁棒性。为了优化算法的性能,采用多(短)链并行的方式代替原来的长链生成方式,提高了算法收敛的稳定性;并对特定模型下的构造过程进行分析,优化了状态空间,提高了算法的搜索效率;同时结合多混合的MCMC方法,进一步提高了算法估计的精确度和收敛速度。仿真结果表明,改进后的算法对波达方向估计的准确性和实时性都有很大提升。