基于再生核希尔伯特空间映射的高维数据特征选择优化算法*

现有过滤型特征选择算法并未考虑非线性数据的内在结构,从而分类准确率远远低于封装型算法,对此提出一种基于再生核希尔伯特空间映射的高维数据特征选算法。首先,基于分支定界法建立搜索树,并对其进行搜索;然后,基于再生核希尔伯特空间映射分析非线性数据的内部结构;最终,根据数据集的内部结构选择最优的距离计算方法。对比仿真实验结果表明,本方法与封装型特征选择算法具有接近的分类准确率,同时在计算效率上具有明显的优势,适用于大数据分析。     

基于迭代平方根容积粒子滤波目标跟踪算法

针对粒子滤波目标跟踪算法粒子退化及跟踪精度问题,提出了一种基于马尔可夫链-蒙特卡罗(MCMC,Markov Chain Monte Carlo)的迭代平方根容积粒子滤波(ISRCPF,iterated square root cubature Kalman particle filter)算法(ISRCPF-MCMC).在该滤波算法中,利用容积数值积分原则计算非线性随机函数的均值和方差,通过正交矩阵分解代替矩阵开方,在生成的粒子滤波建议分布中融入当前量测值,提高对系统后验概率的逼近程度.然后在此基础上融合MCMC抽样算法(MH,Metropolis Hasting)对所选建议分布进行优化,增加粒子多样性,以提高跟踪精度.仿真试验结果表明,ISRCPF-MCMC算法的估计误差与其他算法相比降低至0.403％.     

一种改进的粒子滤波目标跟踪算法*

传统的Condensation跟踪算法使用状态转移分布作为采样粒子的建议分布函数,没有考虑当前的观测值,大量的粒子运算浪费在了那些具有小似然性的区域。针对该问题,提出一种基于Mean Shift以改进建议分布函数的粒子滤波跟踪方法。实验表明,由于有效地利用了当前观测值,改进的算法具有较强的鲁棒性和实时性。     

基于粒子滤波和Galerkin法的改进目标跟踪算法

在粒子滤波框架下,估计的准确性受到建议分布选取的影响很大。传统的粒子滤波通常采用系统转移概率作为建议分布,但传统的建议分布选取方法由于没有考虑新的观测信息,因此不能产生准确的估计值。为此采用一种叫做Galerkin法的数学工具去构造建议分布,依据该方法构造的建议分布相对传统的方法提高了粒子滤波估计的准确性。同时,在新的跟踪算法框架中,将颜色模型和形状模型进行自适应的融合,并提出了一种新的模型更新方法,提高了目标跟踪的稳定性。实验结果证明了该跟踪算法的有效性。     

非线性交互粒子滤波算法

在非线性非高斯系统状态估计问题中，后验概率密度函数的解析形式难以获得，标准粒子滤波算法采用状态转移概率函数代替后验概率作为重要性采样概率密度函数，而未考虑当前观测数据的影响．针对该问题，首先提出了非线性交互多模型算法；然后应用该算法产生重要性采样概率密度函数，设计了新的非线性交互粒子滤波器．新的概率密度函数融入最新观测数据，更接近系统状态后验概率．比较实验表明了所提出算法的有效性．     

Target Tracking Using a Joint Acoustic Video System

In this paper, a multitarget tracking system for collocated video and acoustic sensors is presented. We formulate the tracking problem using a particle filter based on a state-space approach. We first discuss the acoustic state-space formulation whose observations use a sliding window of direction-of-arrival estimates. We then present the video state space that tracks a target's position on the image plane based on online adaptive appearance models. For the joint operation of the filter, we combine the state vectors of the individual modalities and also introduce a time-delay variable to handle the acoustic-video data synchronization issue, caused by acoustic propagation delays. A novel particle filter proposal strategy for joint state-space tracking is introduced, which places the random support of the joint filter where the final posterior is likely to lie. By using the Kullback-Leibler divergence measure, it is shown that the joint operation of the filter decreases the worst case divergence of the individual modalities. The resulting joint tracking filter is quite robust against video and acoustic occlusions due to our proposal strategy. Computer simulations are presented with synthetic and field data to demonstrate the filter's performance     

On the choice of importance distributions for unconstrained and constrained state estimation using particle filter

Recursive state estimation of constrained nonlinear dynamical system has attracted the attention of many researchers in recent years. For nonlinear/non-Gaussian state estimation problems, particle filters have been widely used (Arulampalam et al. [1]). As pointed out by Daum [2], particle filters require a proposal distribution and the choice of proposal distribution is the key design issue. In this paper, a novel approach for generating the proposal distribution based on a constrained Extended Kalman filter (C-EKF), Constrained Unscented Kalman filter (C-UKF) and constrained Ensemble Kalman filter (C-EnkF) has been proposed. The efficacy of the proposed state estimation algorithms using a particle filter is illustrated via a successful implementation on a simulated gas-phase reactor, involving constraints on estimated state variables and another example problem, which involves constraints on the process noise (Rao et al. [10]). We also propose a state estimation scheme for estimating state variables in an autonomous hybrid system using particle filter with Unscented Kalman filter as a proposal and unconstrained Ensemble Kalman filter (EnKF) as a proposal. The efficacy of the proposed state estimation scheme for an autonomous hybrid system is demonstrated by conducting simulation studies on a three-tank hybrid system. The simulation studies underline the crucial role played by the choice of proposal distribution in formulation of particle filters.     

基于证据推理的联合故障检测方法

针对卡方故障检测方法对软故障的检测性能较差, 甚至会导致滤波器发散的问题, 提出一种基于证据推理的联合故障检测方法. 将组合导航中的各子滤波器作为证据, 利用每个子滤波器的状态及协方差构造联合故障检测函数, 并利用联合故障检测函数的概率分布计算基本置信指派, 再将多个证据按D-S 规则进行融合, 根据融合结果进行故障检测. 仿真结果表明, 所提出的方法对硬故障的检测性能与卡方故障检测性能相当, 但对软故障的检测性能要优于卡方故障检测, 可提高组合导航系统的可靠性和精度.

     

A method for outlier detection based on cluster analysis and visual expert criteria

Outlier detection is an important problem occurring in a wide range of areas. Outliers are the outcome of fraudulent behaviour, mechanical faults, human error, or simply natural deviations. Many data mining applications perform outlier detection, often as a preliminary step in order to filter out outliers and build more representative models. In this paper, we propose an outlier detection method based on a clustering process. The aim behind the proposal outlined in this paper is to overcome the specificity of many existing outlier detection techniques that fail to take into account the inherent dispersion of domain objects. The outlier detection method is based on four criteria designed to represent how human beings (experts in each domain) visually identify outliers within a set of objects after analysing the clusters. This has an advantage over other clustering-based outlier detection techniques that are founded on a purely numerical analysis of clusters. Our proposal has been evaluated, with satisfactory results, on data (particularly time series) from two different domains: stabilometry, a branch of medicine studying balance-related functions in human beings and electroencephalography (EEG), a neurological exploration used to diagnose nervous system disorders. To validate the proposed method, we studied method outlier detection and efficiency in terms of runtime. The results of regression analyses confirm that our proposal is useful for detecting outlier data in different domains, with a false positive rate of less than 2% and a reliability greater than 99%.     

基于B样条曲线的GHPF轮廓跟踪算法

针对视觉跟踪中粒子滤波算法的建议性分布函数选择问题,提出一种目标轮廓跟踪的高斯厄米特粒子滤波算法(GHPF).该算法采用B样条曲线描述目标轮廓,建立目标运动模型.利用高斯厄米特滤波器产生建议性分布函数,通过实时融入最新的观测数据来逼近系统状态的后验概率,提高了滤波估计的精度.实验仿真结果验证了所提算法的有效性.     

Partition belief median filter based on Dempster–Shafer theory for image processing

A novel median-type filter controlled by evidence fusion is proposed for removing noise from images. The fusion of evidence based on the Dempster–Shafer evidence theory, providing a way to deal with the uncertainty in the evidence fusion, indicates to what extent a noise is considered. The filter proposed here is obtained as a weighted sum of the current pixel value and the output of the median filter, and the weight is set based on the belief value of the input signal sequence. The efficient step-like function is used to partition the belief space, and the least mean square (LMS) algorithm is applied to obtain the optimal weight for each block. Moreover, to improve the performance, the new filter is recursively implemented. Experimental results have demonstrated that the proposed filter can outperform many well-accepted median-based filters in preserving image details while effectively suppressing impulsive noises, and it also works satisfactorily in reducing Gaussian as well as the mixture of Gaussian and impulsive noise.     

Window annealing for pixel-labeling problems

The pixel labeling problems in computer vision are often formulated as energy minimization tasks. Algorithms such as graph cuts and belief propagation are prominent; however, they are only applicable for specific energy forms. For general optimization, Markov Chain Monte Carlo (MCMC) based simulated annealing can estimate the minima states very slowly.This paper presents a sampling paradigm for faster optimization. First, in contrast to previous MCMCs, the role of detailed balance constraint is eliminated. The reversible Markov chain jumps are essential for sampling an arbitrary posterior distribution, but they are not essential for optimization tasks. This allows a computationally simple window cluster sample. Second, the proposal states are generated from combined sets of local minima which achieve a substantial increase in speed compared to uniformly labeled cluster proposals. Third, under the coarse-to-fine strategy, the maximum window size variable is incorporated along with the temperature variable during simulated annealing. The proposed window annealing is experimentally shown to be many times faster and capable of finding lower energy compared to the previous Gibbs and Swendsen-Wang cut (SW-cut) sampler. In addition, the proposed method is compared with other deterministic algorithms like graph cut, belief propagation, and spectral method in their own specific energy forms. Window annealing displays competitive performance in all domains.     

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.     

多特征融合的退火粒子滤波目标跟踪

针对传统粒子滤波的建议分布没有利用到当前观测信息的缺点,提出了一种基于多特征融合的退火算法来改进建议分布的粒子滤波跟踪方法。该方法解决了高维状态下计算量大和粒子数匮乏问题。采用退火方法在蒙特卡洛重要采样范围内产生更好的建议分布,并用退火似然性抽样来代替简单的先验概率抽样。在似然逼近中,应用颜色和边缘相融合的图像特征属性在不同的退火层加权来产生权重功能函数。用该方法对复杂背景下和存在遮挡情况下的运动目标进行跟踪,结果表明该方法有较高的跟踪精度和较强的稳定性。     

数据链数据过滤器技术研究

本文主要研究数据过滤器技术征数据链中的应用,并往此基础上提出了数据过滤器的关键技术,在数据管理和资源分配等工程领域具有参考价值。     

鲁棒的机器人蒙特卡洛定位算法

提出一种基于粒子滤波器的机器人定位算法. 首先利用一并行扩展卡尔曼滤波器作为粒子预测分布, 将当前观测的部分信息融入, 以改善滤波效果, 减小所需粒子数; 然后提出变密度函数边界的马尔可夫链蒙特卡洛(Markov chain Monte Carlo, MCMC)重采样方法, 以提高粒子的细化能力; 最后结合普通重采样方法, 提出一种改进的MCMC重采样的机器人定位算法, 减少粒子匮乏效应的同时, 提高了定位精度. 实验结果表明, 该算法较传统方法在计算复杂度、定位精度和鲁棒性方面都有显著提高.     

Quasi-interpolation on the Body Centered Cubic Lattice

This paper introduces a quasi-interpolation method for reconstruction of data sampled on the Body Centered Cubic (BCC) lattice. The reconstructions based on this quasi-interpolation achieve the optimal approximation order offered by the shifts of the quintic box spline on the BCC lattice. We also present a local FIR filter that is used to filter the data for quasi-interpolation. We document the improved quality and fidelity of reconstructions after employing the introduced quasi-interpolation method. Finally the resulting quasi-interpolation on the BCC sampled data are compared to the corresponding quasi-interpolation method on the Cartesian sampled data.     

基于Lasso-LDA的酒店用户偏好模型

随着首个在线旅游数据生态共建倡议书的发布,在线评论数据更加真实、准确地表达顾客的客观感受,成为商家和消费者情报的重要来源。结合LDA、TF-IDF算法获取不同类型酒店客户评论特征权值,采用AipNLP获得情感倾向性估计值。利用Lasso算法进行特征筛选构建基于Lasso-LDA的用户偏好模型,将该模型应用于携程网上五种类型用户的偏好分析中。研究结果表明,与传统的多元线性回归及岭回归相比,该模型有更好的预测效果。     

基于优化AGA与辅助边缘粒子滤波的目标跟踪方法

目标跟踪是当今的重要研究课题,广泛应用于通信导航、计算机视觉与自动控制等领域。针对现有的边缘粒子滤波算法目标跟踪可靠性低的问题,提出了一种基于优化自适应遗传算法（Adaptive Genetic Algorithm,AGA）和辅助边缘粒子滤波的目标跟踪方法。在状态空间降维的基础上,推导出崭新的辅助边缘粒子滤波框架,有机地将目标运动的状态划分成线性分量和非线性分量。对于线性分量,沿用卡尔曼滤波估计;对于非线性分量,植入辅助变量构建显式概率分布函数。另一方面,提出了一种的优化AGA实时调节交叉概率与变异概率具有非线性特性,以期筛选出优越的粒子拟合目标的运动状态。实验结果表明,所提出的方法能有效跟踪常见目标,具有估计准确的优点。     

Adaptive square-root transformed unscented FastSLAM with KLD-resampling

The FastSLAM relies on particles sampled from the proposal distribution of underlying Rao–Blackwellized particle filter, and its performance is significantly influenced by the quality and quantity of the particles. In this paper, a new improved FastSLAM is proposed based on transformed unscented Kalman filter (TUKF) and Kullback–Leibler distance (KLD) resampling method. In the proposed algorithm, a square-root extension of TUKF is used to calculate the proposal distribution and to generate credible particles. In addition, during the resampling process, the minimum required number of particles is determined adaptively by bounding the KLD error between the sample-based approximation and true posterior distribution of the robot state. Both numerical simulations and real-world dataset experiments are used to evaluate the performance of the proposed algorithm. The results indicate that the proposed algorithm achieves higher estimation accuracy and computational efficiency than conventional approaches.