FCM与PDA相结合的多传感器多目标跟踪算法

传统的概率数据关联算法(PDA)是在密集杂波环境下的一种良好的多目标跟踪算法,但它是针对单传感器对多目标跟踪的情况下使用,不能直接用于多传感器对多目标的跟踪.针对多传感器多目标跟踪问题,提出一种改进的PDA算法,采用FCM算法预测航迹的聚类中心,然后采用PDA方法对航迹进行跟踪.仿真实验证明此方法能有效地进行多传感器多目标的跟踪.     

马尔可夫链蒙特卡罗算法在采样近似算法中的应用

介绍了马尔可夫链产生的背景,分析了使用马尔可夫链进行采样,指出了马尔可夫链卡罗算法的局限性,对马尔可夫链卡罗算法的研究进行了展望.     

支持无线传感器网络多目标跟踪的聚类数据关联算法研究

多源数据关联问题是无线传感器网络中多传感器数据融合的关键技术之一。联合概率数据关联算法是一种 跟踪多目标的数据关联算法,它不需要任何关于目标和杂波的先验信息,但与其他有关数据关联算法相比,计算机开 销大。基于聚类算法的联合概率数据关联算法在联合概率数据关联算法的基础上,运用模式识别中的聚类思想对传 感器所接收到的量测数据进行聚类,减少有效量测的数目,从而简化了有效矩阵,减少了原有算法的计算量。     

支持无线传感器网络多目标跟踪的最邻近数据关联算法研究

多源数据关联问题是无线传感器网络中多传感器数据融合的关键技术之一。联合概率数据关联算法是一种跟踪多目标的数据关联算法,它不需要任何关于目标和杂波的先验信息,但与其他有关数据关联算法相比,计算机开销大。在构造有效矩阵的过程中基于最邻近方法的联合概率数据关联算法,结合最部近数据关联算法的思想,选取统计距离最小的3个有效量测构成有效矩阵,从而简化了有效矩阵,减少了原有算法的计算量。     

基于联合概率数据关联和粒子滤波的多目标跟踪算法

数据关联一直是多目标跟踪中的核心问题,是实现多目标有效跟踪的关键。介绍了多目标跟踪的基本原理以及联合概率数据关联的基本原理,并且将粒子滤波引入到联合概率数据关联模型中,提出了联合概率数据关联-粒子滤波算法来实现多目标跟踪。仿真结果表明,此算法可以很好的实现固定数目多目标跟踪。     

基于CKF的特征辅助数据关联多目标跟踪

针对密集杂波环境下的多目标近距跟踪问题,提出了一种基于容积卡尔曼滤波(CKF)和特征辅助数据关联的多目标跟踪算法(FADA-CKF).通过特征信息来对传统量测进行扩维,利用扩维后的量测对关联概率进行修正,将特征信息辅助技术融入到联合概率数据关联中,再利用容积卡尔曼滤波(CKF)处理非线性观测量,对目标状态进行估计.将FADA-CKF算法用于近距多目标跟踪场景中,仿真结果表明,改进算法在跟踪精度和误跟率方面要优于传统的JPDA跟踪算法.     

雷达多目标跟踪的关联粒子滤波研究

在整个导弹防御系统中,多目标跟踪是很重要的一项技术,要求系统快速机动地跟踪导弹目标,但系统存在非线性问题,使用传统方法使跟踪偏差大。为解决上述问题,提出在非高斯条件下,把高斯-厄米特粒子滤波算法和联合概率数据关联方法相结合,对多目标跟踪的数据进行关联处理并进行状态估计。利用高斯-厄米特滤波计算的均值、协方差产生密度函数,并生成具有后验特征的粒子。用联合概率数据关联方法进行杂波剔除和数据关联,并对综合的关联粒子滤波算法进行仿真。仿真结果表明,改进方法可以有效解决多目标的准确跟踪问题。     

多目标跟踪数据关联及其改进算法

本文研究了多目标跟踪数据关联问题,针对联合概率数据关联算法的"组合爆炸"现象,介绍了一种改进算法,以较小的计算量直接计算后验概率.蒙特卡罗仿真表明,该算法在对多个目标进行跟踪时具有很好的性能.     

多目标跟踪数据关联及其改进算法

本文研究了多目标跟踪数据关联问题,针对联合概率数据关联算法的“组合爆炸”现象,介绍了一种改进算法,以较小的计算量直接计算后验概率。蒙特卡罗仿真表明,该算法在对多个目标进行跟踪时具有很好的性能。     

多传感器多目标联合概率数据关联研究

提出改进联合概率数据关联算法对多传感器、多目标量测进行同源划分及单一传感器测量数据转换,并采用联合概率数据关联算法求解空间目标轨迹交叉时的数据关联.仿真结果表明,改进联合概率数据关联算法提高了成功关联概率,降低了求解数据关联概率的难度,可以解决密集目标的正确跟踪问题.     

Parallel and interacting Markov chain Monte Carlo algorithm

In many situations it is important to be able to propose N independent realizations of a given distribution law. We propose a strategy for making N parallel Monte Carlo Markov chains (MCMC) interact in order to get an approximation of an independent N-sample of a given target law. In this method each individual chain proposes candidates for all other chains. We prove that the set of interacting chains is itself a MCMC method for the product of N target measures. Compared to independent parallel chains this method is more time consuming, but we show through examples that it possesses many advantages. This approach is applied to a biomass evolution model.     

Automatic glottal inverse filtering with the Markov chain Monte Carlo method

This paper presents a new glottal inverse filtering (GIF) method that utilizes a Markov chain Monte Carlo (MCMC) algorithm. First, initial estimates of the vocal tract and glottal flow are evaluated by an existing GIF method, iterative adaptive inverse filtering (IAIF). Simultaneously, the initially estimated glottal flow is synthesized using the Rosenberg–Klatt (RK) model and filtered with the estimated vocal tract filter to create a synthetic speech frame. In the MCMC estimation process, the first few poles of the initial vocal tract model and the RK excitation parameter are refined in order to minimize the error between the synthetic and original speech signals in the time and frequency domain. MCMC approximates the posterior distribution of the parameters, and the final estimate of the vocal tract is found by averaging the parameter values of the Markov chain. Experiments with synthetic vowels produced by a physical modeling approach show that the MCMC-based GIF method gives more accurate results compared to two known reference methods.     

目标跟踪中的改进Monte Carlo滤波算法

传统的Monte Carlo滤波算法在目标跟踪过程中存在严重的采样贫瘠问题,这直接导致了样本集的退化。为了解决这个问题,提出一种改进的Monte Carlo滤波算法。在样本集建立阶段,采用基于视觉机制的方法建立样本集合,使得样本集在与中心距离较近的地方密集,在离中心较远的地方稀疏,这样的样本集合建立方法能够更准确地反映人眼对事物的感知;在样本集传播阶段,获得一个区分样本优劣的阈值,将样本集合分为优劣两种,用重采样的方法对优样本集合采样,采样半数样本,用随机抽样的方法补充其余半数样本,实验结果表明,这种方法可以很好地解决样本退化的问题。     

Population Markov Chain Monte Carlo

Stochastic search algorithms inspired by physical and biological systems are applied to the problem of learning directed graphical probability models in the presence of missing observations and hidden variables. For this class of problems, deterministic search algorithms tend to halt at local optima, requiring random restarts to obtain solutions of acceptable quality. We compare three stochastic search algorithms: a Metropolis-Hastings Sampler (MHS), an Evolutionary Algorithm (EA), and a new hybrid algorithm called Population Markov Chain Monte Carlo, or popMCMC. PopMCMC uses statistical information from a population of MHSs to inform the proposal distributions for individual samplers in the population. Experimental results show that popMCMC and EAs learn more efficiently than the MHS with no information exchange. Populations of MCMC samplers exhibit more diversity than populations evolving according to EAs not satisfying physics-inspired local reversibility conditions.     

Front-view vehicle detection by Markov chain Monte Carlo method

In this paper, we propose a new vehicle detection approach based on Markov chain Monte Carlo (MCMC). We mainly discuss the detection of vehicles in front-view static images with frequent occlusions. Models of roads and vehicles based on edge information are presented, the Bayesian problem's formulations are constructed, and a Markov chain is designed to sample proposals to detect vehicles. Using the Monte Carlo technique, we detect vehicles sequentially based on the idea of maximizing a posterior probability (MAP), performing vehicle segmentation in the meantime. Our method does not require complex preprocessing steps such as background extraction or shadow elimination, which are required in many existing methods. Experimental results show that the method has a high detection rate on vehicles and can perform successful segmentation, and reduce the influence caused by vehicle occlusion.     

Combining particle MCMC with Rao-Blackwellized Monte Carlo data association for parameter estimation in multiple target tracking

We consider state and parameter estimation in multiple target tracking problems with data association uncertainties and unknown number of targets. We show how the problem can be recast into a conditionally linear Gaussian state-space model with unknown parameters and present an algorithm for computationally efficient inference on the resulting model. The proposed algorithm is based on combining the Rao-Blackwellized Monte Carlo data association algorithm with particle Markov chain Monte Carlo algorithms to jointly estimate both parameters and data associations. Both particle marginal Metropolis–Hastings and particle Gibbs variants of particle MCMC are considered. We demonstrate the performance of the method both using simulated data and in a real-data case study of using multiple target tracking to estimate the brown bear population in Finland.     

Adaptive importance sampling in signal processing

In Bayesian signal processing, all the information about the unknowns of interest is contained in their posterior distributions. The unknowns can be parameters of a model, or a model and its parameters. In many important problems, these distributions are impossible to obtain in analytical form. An alternative is to generate their approximations by Monte Carlo-based methods like Markov chain Monte Carlo (MCMC) sampling, adaptive importance sampling (AIS) or particle filtering (PF). While MCMC sampling and PF have received considerable attention in the literature and are reasonably well understood, the AIS methodology remains relatively unexplored. This article reviews the basics of AIS as well as provides a comprehensive survey of the state-of-the-art of the topic. Some of its most relevant implementations are revisited and compared through computer simulation examples.     

基于马尔可夫链蒙特卡罗采样的双密钥盲水印算法

为了更好地提高水印算法的安全性,提出了一种基于两种形式密钥的强鲁棒盲水印算法。首先对水印加密,然后将每块载体的第一个奇异值组成矩阵Q再分块离散小波变换(DWT)获得四个子带,通过对四个子带进行马尔可夫链蒙特卡罗(MCMC)采样决定第k个水印位量化嵌入到矩阵Q的第k块低频、水平、垂直和高频子带中的一个并记录当前嵌入子带的密钥位,这样做不仅使水印位随机分配,而且提高了水印算法的安全性。实验结果表明,所提算法在满足不可见性的条件下,不仅对常规的图像攻击具备较强的鲁棒性,而且在水印嵌入过程中通过MCMC采样实现了用不同的密钥嵌入,提高了水印算法的安全性。     

Overview of Bayesian sequential Monte Carlo methods for group and extended object tracking

This work presents the current state-of-the-art in techniques for tracking a number of objects moving in a coordinated and interacting fashion. Groups are structured objects characterized with particular motion patterns. The group can be comprised of a small number of interacting objects (e.g. pedestrians, sport players, convoy of cars) or of hundreds or thousands of components such as crowds of people. The group object tracking is closely linked with extended object tracking but at the same time has particular features which differentiate it from extended objects. Extended objects, such as in maritime surveillance, are characterized by their kinematic states and their size or volume. Both group and extended objects give rise to a varying number of measurements and require trajectory maintenance. An emphasis is given here to sequential Monte Carlo (SMC) methods and their variants. Methods for small groups and for large groups are presented, including Markov Chain Monte Carlo (MCMC) methods, the random matrices approach and Random Finite Set Statistics methods. Efficient real-time implementations are discussed which are able to deal with the high dimensionality and provide high accuracy. Future trends and avenues are traced.     

参数反演的微分进化蒙特卡洛算法

针对参数反演问题,提出了微分进化蒙特卡洛算法。此方法是在贝叶斯推理的蒙特卡洛算法的基础上融入微分进化思想。与传统的蒙特卡洛算法相比,此方法有效地缩减了迭代次数,提高了反演精度并具有一定的稳定性。