非均匀稀疏采样环境的改进高斯粒子滤波方法

粒子滤波（PF）技术的研究一直是非线性滤波领域的热点和难点问题,针对非均匀稀疏采样环境下传感器观测的滤波估计问题,提出了一种结合目标运动特性的改进型高斯粒子滤波方法。在该方法中,首先深入分析了传统粒子滤波不能有效对非均匀稀疏采样观测数据进行有效处理的原因,通过引入目标观测、目标观测的有效时间间隔、目标速度等目标特性,综合改善高斯粒子滤波器在时间更新阶段预测粒子和预测协方差估计的准确性,从而提高观测更新阶段重要性密度函数的估计精度,实现对目标状态的精确估计。实验结果表明,对于一维非线性非高斯例子,提出方法要稍好于传统的PF、辅助粒子滤波（APF）和高斯粒子滤波（GPF）;而对于实际的非均匀稀疏采样观测样本,提出方法要远好于PF、APF和GPF,能够有效对目标进行状态估计。      

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.     

基于粒子滤波器的非线性估计方法

介绍基于粒子滤波器的非线性估计方法.采用正则化粒子滤波器来缓解粒子滤波器重采样造成的问题,改进了粒子滤波器的性能.在一种典型的非静态增长模型下比较EKF,UKF,PF和RPF的滤波性能差异.仿真结果表明,PF在滤波精度方面优于EKF和UKF,而RPF在精度和计算复杂度等方面均优于PF.     

一种目标特性辅助的积分粒子滤波新方法

针对非均匀稀疏采样环境下目标跟踪中的非线性滤波问题,提出了一种基于Gauss-Hermite积分和目标特性辅助的积分粒子滤波新方法(AQPF).在该方法中,构建了基于Gauss-Hermite积分的积分点概率密度函数作为重要性密度函数,同时,在时间更新阶段引入目标观测、目标观测的有效时间间隔、目标速度等目标特性,综合改善滤波器中预测粒子和预测协方差估计的准确性和粒子的多样性,有效提高目标状态的估计性能.实验结果表明,提出方法的估计性能要明显好于无迹kalman滤波(UKF)、积分kalman滤波(QKF)、粒子滤波(PF)、辅助粒子滤波(APF)和高斯粒子滤波(GPF),能够有效对目标状态进行估计.     

单站目标被动跟踪的滤波器方法研究

为了实现对单站目标的被动跟踪,分析并比较了扩展Kalman滤波器和粒子滤波器在非线性估计方面的性能,并且针对粒子滤波器存在的粒子退化现象,引入改进的重采样算法和基于无迹变换的滤波算法.仿真实验分别比较了几种滤波器在目标做匀速、匀加速、变加速情况下距离和速度滤波的均方根误差,结果表明粒子滤波器滤波性能优于扩展的Kalman滤波器,改进的重采样算法和基于无迹变换的粒子滤波器可以有效改善估计精度.     

基于空间分布和时间序列分析的粒子滤波算法

针对粒子滤波存在的粒子贫化问题,提出了一种改进的重采样粒子滤波算法.在重采样步骤中基于采样粒子集的空间分布引入时间序列分析,选取相关度最高的粒子进行传递,避免了只关注采样粒子权值的传统重采样算法中仅复制大权值粒子而任意丢弃小权值粒子的缺陷,因此能够消弱粒子贫化现象,提高算法的估计精度.在理论上利用两样本Kolmogorov-Smirnov检验原理证明了改进算法重采样后的粒子集和采样前的粒子集来自同一总体.仿真结果表明,尤其是在初始采样粒子数目较小时,该算法在非线性系统状态估计中的精度优于传统的粒子滤波算法.     

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。     

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。     

一种新的改进辅助变量粒子滤波算法北大核心CSCD

针对辅助变量粒子滤波(AVPF)对状态估计无法获得较好的滤波精度的问题,本文提出了一种改进的辅助变量粒子滤波算法.将正则化的思想引入到辅助变量粒子滤波的重采样中,在重采样中将离散的概率分布函数近似为连续的分布函数,该方法不仅保留了AVPF在重采样之前依据似然值的大小对原粒子集中的各个权值进行修正,从经过平滑后的后验密度中重采样的特点,在重采样中引入正则化思想后还能够保持粒子的多样性,增加有效样本数目,能够有效抑制样本退化.针对一个被广泛采用的双峰,高度非线性的系统模型,在选取不同的过程噪声下,进行Monte Carlo仿真实验.仿真实验表明,改进的辅助变量粒子滤波具有更好滤波精度.     

一种高斯分布的调整粒子滤波算法

粒子滤波(PF)方法与传统的非线性滤波方法如扩展卡尔曼(EKF)类方法相比,无需计算Jacobi矩阵,受初始状态影响小而稳定性强,因此粒子滤波方法研究成为非线性滤波研究的热点问题.但在可观测性较差的非线性系统滤波中常用的普通粒子滤波方法(GPF)易受退化、采样枯竭等因素影响而在可能会引起滤波误差大甚至不收敛等问题.本文提出了一种高斯分布的调整粒子滤波跟踪算法,即在粒子再采样后加上一定的高斯噪声分布调整粒子分布,以产生更接近真实状态的粒子.经过只测角定位跟踪举例仿真表明,本文方法具有较高的滤波精度.     

Improved particle filter based on fine resampling algorithm

In order to solve particle degeneracy phenomenon and simultaneously avoid sample impoverishment,this paper proposed an improved particle filter based on fine resampling algorithm for general case,calle...     

MCMC粒子滤波算法研究及应用

粒子滤波算法是一种基于贝叶斯估计的蒙特卡罗方法,适用于非线性非高斯系统的分析,被广泛应用于跟踪、定位等问题的研究中。为了解决粒子滤波算法在重采样后,丧失粒子多样性的问题,本文在粒子滤波算法的重采样步骤后,加入了马尔可夫链蒙特卡罗（Markov Chain Monte Carlo,简称MCMC）移动步骤,增加粒子的多样性。利用粒子滤波算法和MCMC粒子滤波算法对目标跟踪问题进行了仿真,并且通过分析仿真实验结果,比较了两种算法的性能,结果说明加入MCMC粒子滤波算法的性能优于粒子滤波算法。     

量子进化粒子滤波算法及其在说话人跟踪中的应用

以说话人跟踪问题作为应用背景,针对非线性、非高斯随机系统的状态估计问题,本文将量子进化算法和粒子滤波相结合,提出了一种量子进化粒子滤波算法.该算法采用量子编码来表示粒子;通过模拟量子坍塌产生粒子集,同时引入量子变异与量子交叉操作来保持粒子的多样性,从而提高了算法的滤波性能.计算机仿真与说话人跟踪实验结果表明,量子进化粒子滤波算法比通常的粒子滤波算法具有更好的滤波性能.     

基于人工蜂群算法的粒子滤波目标跟踪

针对基于粒子滤波的视频目标跟踪算法中由于粒子重采样过程而导致粒子贫化的问题,提出了一种基于人工蜂群算法的粒子滤波目标跟踪算法,利用群体智能的特点使得粒子集在重采样前得到优化,保持了粒子的多样性,从而解决了粒子贫化问题,同时增加了有效粒子的数目.实验结果表明,基于人工蜂群算法的粒子滤波跟踪算法,比标准粒子滤波跟踪算法所需粒子数更少,对目标遮挡、较复杂背景有较好的跟踪效果.     

基于CPFDE的目标跟踪算法研究

在目标跟踪中,为了克服粒子滤波的粒子退化和贫化问题,提高滤波精度,文中将差分演化算法与容积粒子滤波相结合,形成了差分演化容积粒子滤波算法。在粒子进行先验更新时, 使用容积卡尔曼滤波算法融入当前时刻的量测信息并用其来产生重要性密度函数,并且在重采样阶段,用差分演化算法对根据重要性密度函数抽取的采样粒子做优化操作,从而克服粒子滤波存在的粒子退化及贫化问题,提高滤波性能。实验结果表明,和粒子滤波、无迹粒子滤波、容积粒子滤波相比,该算法有着更高的滤波精度和更好的稳定性,并且能够提高雷达机动目标跟踪的精确性。     

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.     

基于随机摄动再采样的粒子概率假设密度滤波器

作为概率假设密度滤波的典型实现方式,粒子概率假设密度滤波器无需线性高斯等先验假设,因而在多目标跟踪中得到了广泛的应用。为解决粒子退化问题并保持粒子规模,该滤波器引入了重采样机制,然而,该重采样机制易引起粒子多样性耗尽,导致粒子贫化问题产生。为解决这一问题,该文提出一种新的基于随机摄动再采样的粒子概率假设密度滤波器。首先,全面分析了粒子概率假设密度滤波因粒子贫化问题导致目标失跟的过程。然后设计了一种随机摄动再采样算法,该算法在重采样导致粒子多样性缺失时,根据源粒子的位置与复制次数随机产生相应数目的新粒子,并对源粒子进行删减,其可在保留源粒子信息的前提下保持粒子的多样性。最后,该文将该算法纳入概率假设密度滤波框架,提出了一种新的粒子概率假设密度滤波器。仿真结果表明该滤波器在不显著增加运行时间的前提下能够克服粒子贫化问题,相比标准的粒子概率假设密度滤波器具有更好的跟踪性能。     

Resampling algorithms and architectures for distributed particle filters

In this paper, we propose novel resampling algorithms with architectures for efficient distributed implementation of particle filters. The proposed algorithms improve the scalability of the filter architectures affected by the resampling process. Problems in the particle filter implementation due to resampling are described, and appropriate modifications of the resampling algorithms are proposed so that distributed implementations are developed and studied. Distributed resampling algorithms with proportional allocation (RPA) and nonproportional allocation (RNA) of particles are considered. The components of the filter architectures are the processing elements (PEs), a central unit (CU), and an interconnection network. One of the main advantages of the new resampling algorithms is that communication through the interconnection network is reduced and made deterministic, which results in simpler network structure and increased sampling frequency. Particle filter performances are estimated for the bearings-only tracking applications. In the architectural part of the analysis, the area and speed of the particle filter implementation are estimated for a different number of particles and a different level of parallelism with field programmable gate array (FPGA) implementation. In this paper, only sampling importance resampling (SIR) particle filters are considered, but the analysis can be extended to any particle filters with resampling.     

Improved Particle Filter Resampling Architectures

The most challenging aspect of particle filtering hardware implementation is the resampling step. This is because of high latency as it can be only partially executed in parallel with the other steps of particle filtering and has no inherent parallelism inside it. To reduce the latency, an improved resampling architecture is proposed which involves pre-fetching from the weight memory in parallel to the fetching of a value from a random function generator along with architectures for realizing the pre-fetch technique. This enables a particle filter using M particles with otherwise streaming operation to get new inputs more often than 2M cycles as the previously best approach gives. Results show that a pre-fetch buffer of five values achieves the best area-latency reduction trade-off while on average achieving an 85% reduction in latency for the resampling step leading to a sample time reduction of more than 40%. We also propose a generic division-free architecture for the resampling steps. It also removes the need of explicitly ordering the random values for efficient multinomial resampling implementation. In addition, on-the-fly computation of the cumulative sum of weights is proposed which helps reduce the word length of the particle weight memory. FPGA implementation results show that the memory size is reduced by up to 50%.