动态数据校正提升孤岛微电网频率控制性能

在复杂的工业生产过程中,控制系统不可能工作在理想状态,其总会受到外界的影响,比如,在对反馈信号进行测量时,控制系统不可避免的会受到测量噪声的影响。为了降低测量噪声的影响,通常会使用各种各样的滤波技术以提升控制系统的性能。本文以微电网的频率控制系统为研究对象,考虑了当反馈回路有高斯分布测量噪声和非高斯分布测量噪声的两种情况,针对这两种测量噪声信号在模型中引入了动态数据校正滤波技术,分别对比了有无使用动态数据校正滤波技术时电网的频率偏差的方差大小,以此验证了动态数据校正滤波技术在微电网反馈控制回路中可有效抑制测量噪声的影响从而提升微电网频率控制性能。     

卡尔曼粒子滤波的视频车辆跟踪算法研究

近年来,视频车辆跟踪作为城市智能交通系统（ITS）的一个关键技术受到关注。本文针对传统粒子滤波的非线性、非高斯性可能导致跟踪过程的不稳健性,提出一种基于卡尔曼粒子滤波的视频车辆跟踪算法,该算法利用基于重要区域的目标颜色直方图统计模型对视频车辆目标进行建模,并将其应用于卡尔曼滤波更新中,通过采用Mean Shift算法将卡尔曼滤波器引用到粒子滤波器当中,对车辆的运行轨迹进行校正,实现了局部线性滤波,实现了在保持跟踪系统整体上的非线性、非高斯性的同时,兼顾其局部的线性高斯特性。实验结果表明,本文所提出的方法与传统粒子滤波方法相比,能够更准确地对车辆进行跟踪,同时保证了在复杂环境下性能的稳健性。     

一种基于改进UPF的运动声阵列交互多模型目标跟踪方法

针对运动声阵列在有色噪声环境中的非线性滤波跟踪实时性问题,提出一种基于改进粒子滤波的交互式多模型目标跟踪(IMM-IUPF)方法.该方法采用最小斜度单形无迹变换结合变尺度无迹变换的 Sigma点选取策略,对标准无迹粒子滤波(UPF)进行改进,大幅缩减UPF样本选取计算量;将改进的UPF与交互式多模型(IMM)相结合,通过定义自适应增益修正系数,弥补样本点缩减造成的精度降低问题;最终通过与传统的基于IMM的粒子滤波(IMM-PF)和基于IMM的无迹粒子滤波(IMM-UPF)进行Matlab数值仿真对比验证所提出算法的实用性.     

MCMC粒子滤波的GPS定位数据处理算法

针对基于传统粒子滤波的GPS(Global positioning system)定位数据处理方法存在粒子退化的问题,研究了基于马尔可夫链蒙特卡罗(Markov chain Monte Carol,MCMC)粒子滤波的GPS定位数据处理算法,引入典型的MCMC方法—Metropolis Hastings(M-H)抽样算法.利用观测伪距非高斯误差分布,建立重要密度函数,将MCMC粒子滤波与建立的GPS系统非线性状态空间模型结合.实测数据实验结果表明,MCMC粒子滤波可有效抑制粒子退化,解决了GPS定位数据滤波这一非线性非高斯问题,避免了噪声的高斯假设和非线性部分的线性化误差,与基于传统粒子滤波的GPS定位数据处理方法相比,该方法降低了定位数据经纬度和速度估计误差,获得了更高的定位精度,并能够在GPS信号质量较差情况下,对GPS定位数据有效滤波,保证载体在此期间内保持较高的位置精度.     

WSN 中基于粒子滤波的目标跟踪研究

针对无线传感器网络中仅有角度测量的目标跟踪问题,详细分析其定位过程,提出一种基于改进粒子滤波算法的新解决方案.方案采用高斯混合模型(GMM)代替单一模型近似概率密度分布,融入粒子滤波算法,提议分布采用SRUKF-PF算法更新粒子.算法中增加改进措施以提高滤波性能如:残差重采样算法用于重采样、K-means算法用于构建预估起始点、EM算法用于拟合GMM近似等.仿真结果验证了方案的有效性.     

基于IMM多传感器顺序粒子滤波跟踪机动目标算法

针对交互式多模型粒子滤波在跟踪机动目标时精度受限问题,提出一种基于交互式多模型(IMM)的多传感器顺序粒子滤波算法。采用IMM机制实现目标运动模式的确认;在合理利用单传感器量测和多传感器量测中冗余和互补信息的基础上,引入顺序重抽样方法改善粒子分布,并将改善后的粒子应用于IMM粒子滤波算法框架。仿真实验结果表明:新算法能够估计出强机动目标状态,且精度明显优于标准IMM粒子滤波算法。     

基于Mean-Shift的卡尔曼粒子滤波车辆跟踪算法研究

在视频车辆跟踪算法中针对传统粒子滤波的非线性、非高斯性可能导致跟踪过程的不准确性,提出一种基于Mean-Shift的卡尔曼(Kalman)粒子滤波算法。该算法利用建立基于目标颜色直方图特征模型对视频车辆目标进行建模,并将其与Kalman滤波相结合进行更新;通过采用Mean Shift算法将Kalman滤波器引用到粒子滤波器当中,通过预测迭代,从而达到对车辆的运行轨迹的修正。将先验信息预测与粒子滤波相结合在保持跟踪系统整体上的非线性、非高斯性,兼顾了卡尔曼滤波局部的线性高斯特性。实验结果表明,该方法与传统粒子滤波方法相比,具有较好的实时性和较高的准确率,能够准确稳定地对目标车辆进行跟踪。     

基于粒子滤波的交通仿真系统

针对现有多数交通仿真系统在交通状态估计问题精确度不高的缺陷,引入粒子滤波方法对交通状态进行预测估计,设计并实现基于粒子滤波的交通仿真系统。首先介绍系统粒子滤波的初始化、粒子状态转移过程、权值计算及归一化和重采样操作的设计,然后将实测交通数据通过粒子滤波算法引入模型以提高状态估计的能力,最后基于微观交通仿真软件MovSim实现该系统。实验表明：基于粒子滤波的交通仿真系统具有较强的预测稳定性和准确性。     

粒子滤波在MEMS陀螺仪初始对准中的应用

针对微机电系统MEMS(Micro-Electro-Mechanical-System)陀螺仪的随机误差,引入了粒子滤波处理MEMS IMU的输出数据.借助于机动目标的Singer模型建立了系统状态方程,论文讨论了粒子滤波算法在MEMS IMU滤波处理的应用,详细描述了算法的推导过程.应用经典卡尔曼滤波和粒子滤波分别处...     

新的室内移动机器人自定位方法

针对现有室内移动机器人自定位方法中存在的定位精度不高,随时间积累定位误差增大,复杂室内环境下信号存在多径效应和非视距效应等问题,提出了一种基于蒙特卡罗定位(MCL)的新的移动机器人自定位方法。首先,通过分析基于无线射频识别(RFID)技术的移动机器人自定位系统,建立机器人运动模型;然后,通过分析基于接收信号强度指示(RSSI)的移动机器人自定位系统,提出机器人移动过程的观测模型;最后,针对粒子滤波定位执行效率不高的问题,提出粒子剔除策略和依据粒子方位赋予粒子权值策略,提高系统的定位精度和执行效率。仿真实验表明,机器人在移动过程中的自定位误差在X轴和Y轴方向上为3 cm,传统定位算法误差为6cm,新算法定位精度提高近1倍,且算法具有很好的鲁棒性。     

基于鲁棒估计的动态数据校正方法

针对目前动态数据校正方法存在的缺陷，本文在以前的工作基础上把基于鲁棒估计原理构造的鲁棒估计函数用于含异常点类型的过失误差的数据校正，Monte Carlo模拟结果及对TE问题的校正计算结果表明，这种基于鲁棒估计的过换误差侦破和数据协调同步方法可以在得到协调数据的同时很准确的侦破和识别出测量数据中所含的过失误差，具有较强的优越性。     

Field of Particle Filters for Image Inpainting

We present a novel algorithm for solving the image inpainting problem based on a field of locally interacting particle filters. Image inpainting, also known as image completion, is concerned with the problem of filling image regions with new visually plausible data. In order to avoid the difficulty of solving the problem globally for the region to be inpainted, we introduce a field of local particle filters. The states of the particle filters are image patches. Global consistency is enforced by a Markov random field image model which connects neighbouring particle filters. The benefit of using locally interacting particle filters is that several competing hypotheses on inpainting solutions are kept active, allowing the method to provide globally consistent solutions on problems where other local methods may fail. We provide examples of applications of the developed method.     

State estimation of nonlinear dynamic systems using weighted variance-based adaptive particle swarm optimization

New heuristic filters are proposed for state estimation of nonlinear dynamic systems based on particle swarm optimization (PSO) and differential evolution (DE). The methodology converts state estimation problem into dynamic optimization to find the best estimate recursively. In the proposed strategy the particle number is adaptively set based on the weighted variance of the particles. To have a filter with minimal parameter settings, PSO with exponential distribution (PSO-E) is selected in conjunction with jDE to self-adapt the other control parameters. The performance of the proposed adaptive evolutionary algorithms i.e. adaptive PSO-E, adaptive DE and adaptive jDE is studied through a comparative study on a suite of well-known uni- and multi-modal benchmark functions. The results indicate an improved performance of the adaptive algorithms relative to original simple versions. Further, the performance of the proposed heuristic filters generally called adaptive particle swarm filters (APSF) or adaptive differential evolution filters (ADEF) are evaluated using different linear (nonlinear)/Gaussian (non-Gaussian) test systems. Comparison of the results to those of the extended Kalman filter, unscented Kalman filter, and particle filter indicate that the adopted strategy fulfills the essential requirements of accuracy for nonlinear state estimation.     

一种新的动态过程数据校正方法

数据校正技术能有效地提高测量数据的质量。本文提出了一种基于过程模型少统计分析和动态过程数据校正方法,该方法能同时进行过失误差检测并最终得到各个变量的校正值。对这种方法进行了仿真研究,仿真结果证实了该方法的有效性。     

Marginalized adaptive particle filtering for nonlinear models with unknown time-varying noise parameters

Knowledge of the noise distribution is typically crucial for the state estimation of general state-space models. However, properties of the noise process are often unknown in the majority of practical applications. The distribution of the noise may also be non-stationary or state dependent and that prevents the use of off-line tuning methods. For linear Gaussian models, Adaptive Kalman filters (AKF) estimate unknown parameters in the noise distributions jointly with the state. For nonlinear models, we provide a Bayesian solution for the estimation of the noise distributions in the exponential family, leading to a marginalized adaptive particle filter (MAPF) where the noise parameters are updated using finite dimensional sufficient statistics for each particle. The time evolution model for the noise parameters is defined implicitly as a Kullback–Leibler norm constraint on the time variability, leading to an exponential forgetting mechanism operating on the sufficient statistics. Many existing methods are based on the standard approach of augmenting the state with the unknown variables and attempting to solve the resulting filtering problem. The MAPF is significantly more computationally efficient than a comparable particle filter that runs on the full augmented state. Further, the MAPF can handle sensor and actuator offsets as unknown means in the noise distributions, avoiding the standard approach of augmenting the state with such offsets. We illustrate the MAPF on first a standard example, and then on a tire radius estimation problem on real data.     

基于改进PSO的发酵过程同步串联混合建模

准确可靠的过程模型是实现发酵过程优化的基础和前提. 对于反应机理复杂的发酵过程,串联混合建模是一种相对有效的建模方法, 但现有方法需要利用插值所得的数据进行中间变量黑箱模型的构建, 较大程度地影响了所建混合模型的泛化性能. 为此,提出一种可将黑箱模型构建问题转化为动态模型参数辨识问题的同步串联混合建模方法, 从而避免了现有方法需利用插值数据来构建黑箱模型的不足; 通过引入多精英学习策略和惯性权重自适应调整策略, 构造了一种改进的粒子群优化(Particle swarm optimization, PSO)算法自适应多精英学习PSO (Adaptive multi-elite learning PSO, AMLPSO)算法,并采用该算法求取黑箱模型的参数; 借鉴均匀设计思想确定黑箱模型的结构. 利用诺西肽分批发酵过程实际生产数据进行实验研究, 结果验证了所提方法的有效性.     

Heuristic particle filter: applying abstraction techniques to the design of visual tracking algorithms

Abstract: Many real‐world visual tracking applications have a high dimensionality, i.e. the system state is defined by a large number of variables. This kind of problem can be modelled as a dynamic optimization problem, which involves dynamic variables whose values change in time. Most applied research on optimization methods have focused on static optimization problems but these static methods often lack explicit adaptive methodologies. Heuristics are specific methods for solving problems in the absence of an algorithm for formal proof. Metaheuristics are approximate optimization methods which have been applied to more general problems with significant success. However, particle filters are Monte Carlo algorithms which solve the sequential estimation problem by approximating the theoretical distributions in the state space by simulated random measures called particles. However, particle filters lack efficient search strategies. In this paper, we propose a general framework to hybridize heuristics/metaheuristics with particle filters properly. The aim of this framework is to devise effective hybrid visual tracking algorithms naturally, guided by the use of abstraction techniques. Resulting algorithms exploit the benefits of both complementary approaches. As a particular example, a memetic algorithm particle filter is derived from the proposed hybridization framework. Finally, we show the performance of the memetic algorithm particle filter when it is applied to a multiple object tracking problem.     

粒子滤波理论、方法及其在多目标跟踪中的应用

本文梳理了粒子滤波理论基本内容、发展脉络和最新研究进展, 特别是对其在多目标跟踪应用中的一系列难点问题与主流解决思路进行了详细分析和报道. 常规粒子滤波研究重点主要围绕重要性采样函数、计算效率、权值退化/样本匮乏和复杂系统建模展开. 作为一类复杂估计问题,多目标跟踪一方面需要准确的目标新生/消亡与演变、虚警/漏检等建模技术, 另一方面需要多传感器信息融合、航迹管理等复杂决策方法.暨有限集统计学应用于多目标跟踪后,粒子 滤波进入一个新的发展阶段---随机集粒子滤波.基于不同的背景假设,可以构建不同近似形式的随机集贝 叶斯滤波器并采用粒子滤波实现.但机动目标、未知场景、多目标航迹管理以及跟踪性能评价等仍是多 目标粒子滤波的研究难点和重点.     

Fast and accurate SLAM with Rao–Blackwellized particle filters

Rao–Blackwellized particle filters have become a popular tool to solve the simultaneous localization and mapping problem. This technique applies a particle filter in which each particle carries an individual map of the environment. Accordingly, a key issue is to reduce the number of particles and/or to make use of compact map representations. This paper presents an approximative but highly efficient approach to mapping with Rao–Blackwellized particle filters. Moreover, it provides a compact map model. A key advantage is that the individual particles can share large parts of the model of the environment. Furthermore, they are able to reuse an already computed proposal distribution. Both techniques substantially speed up the overall filtering process and reduce the memory requirements. Experimental results obtained with mobile robots in large-scale indoor environments and based on published standard datasets illustrate the advantages of our methods over previous mapping approaches using Rao–Blackwellized particle filters.     

A novel real-time adaptive suboptimal recursive state estimation scheme for nonlinear discrete dynamic systems with non-Gaussian noise

A real-time state filtering and prediction scheme which is adaptive, recursive, and suboptimal is proposed for discrete time nonlinear dynamic systems with either Gaussian or non-Gaussian noise. The proposed scheme (PR) estimates states adaptively whenever both the observation is available and there exists a non-zero and finite number of real state roots of the observation model, otherwise the PR estimates states non-adaptively. The PR state transition and observation functions are as general as the state transition and observation functions for particle filters. The PR is based upon discrete noise approximation, state quantization, and a suboptimal implementation of multiple hypothesis testing. The PR first detects state estimate divergence points along the time axis, and then state estimate divergences are prevented by introducing new admissible state quantization levels; whereas the extended Kalman filter (EKF), sampling importance resampling (SIR) particle filter (bootstrap filter), and auxiliary sampling importance resampling (ASIR) particle filter produce diverging state estimates from actual state values for many dynamic models. The PR uses state transition functions in order to calculate transition probabilities from gates to gates. If these transition probabilities are somehow available, then state transition functions are not needed for state estimation with the PR; whereas state transition functions are necessary for state estimation with both particle filters and the EKF. The PR is very suitable for state estimation with either constraints imposed on state estimates or missing observations. The PR is more general than grid-based estimation approaches. Monte Carlo simulations have shown the effectiveness of the PR, that is, the PR performance is better than the performances of the EKF, SIR, and ASIR particle filters for many nonlinear models with white Gaussian noise, four examples of which are presented in the paper.