Description: ：介绍了扩展卡尔曼滤波算法和无迹变换(unscented transformation，UT)算法，并对扩展卡尔曼滤波算法(EKF)和无
迹卡尔曼滤波算法(UKF)进行比较，阐明了UKF优于EKF。在此基础上，提出了一种基于Unscented变换(uT)的高斯和滤
波算法，该算法首先通过合并准则得到适当个数的混合高斯模型，逼近系统中非高斯噪声的概率密度-: Introduction of the extended Kalman filter algorithm and unscented transform (unscented transformation, UT) algorithm, the extended Kalman filter algorithm (EKF) and unscented Kalman filter (UKF) for comparison to clarify the UKF is superior to EKF. On this basis, we propose an approach based on Unscented Transform (uT) and the Gaussian filtering algorithm, which first of all, by merging the appropriate number of criteria to be a mixture of Gaussian model, which was close to the system of the Central African Gaussian noise probability density Platform: |
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Author:lyh |
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Description: 随着这些年计算机硬件水平的发展, 计算速度的提高, 源自序列蒙特卡罗方法的蒙特卡罗粒子滤波方法的应用研究又重新活跃起来。本文的这种蒙特卡罗粒子滤波算法是利用序列重要性采样的概念, 用一系列离散的带权重随机样本近似相
应的概率密度函数。由于粒子滤波方法没有像广义卡尔曼滤波方法那样对非线性系统做线性化的近似, 所以在非线性状态估计方面比广义卡尔曼滤波更有优势。在很多方面的应用已经逐渐有替代广义卡尔曼滤波的趋势。-With the years the level of computer hardware development, the speed of calculation, derived from the sequence of the Monte Carlo method, Monte Carlo particle filter method applied research has once again become active again. In this paper, this kind of Monte Carlo particle filter is to use the concept of sequence of the importance of sampling, using a series of discrete random sample with weights similar to the corresponding probability density function. Since the particle filtering method is not as broad as Kalman filtering method for nonlinear system to do linear approximation, nonlinear state estimation in the generalized Kalman filter than an advantage. Applications in many areas has been gradually generalized Kalman filter has an alternative trend. Platform: |
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Author:阳关 |
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Description: 本文的题目是改进的粒子滤波在组合导航中的应用研究。文档可用caj打开。
本课题首先研究了GPS/DR车载定位系统的组合模型，然后在分析了非线性滤波的基础上，引入了粒子滤波。粒子滤波是一种基于递推计算的序列蒙特卡罗算法，它采用一组从概率密度函数上随机抽取的并附带相关权值的粒子集来逼近后验概率密度，从而不受非线性、非高斯问题的限制。虽然粒子滤波存在诸多优点，然而它仍然存在诸如粒子数匿乏、滤波性能不高、实时性差等问题。-The title of this article is to improve the particle filter in the navigation of the applied research. CAJ can be used to open the document. This issue initially on the GPS/DR Vehicle Location System portfolio model, and then the analysis of nonlinear filtering based on the introduction of a particle filter. Particle filter is a recursive calculation based on Sequential Monte Carlo algorithm, it uses a set of probability density function from random samples and weights attached to the relevant set of particles to approximate a posteriori probability density, and thus not subject to non-linear, the issue of non-Gaussian constraints. Although there are many advantages of particle filter, yet it still exists, such as particle number Punic poor, filter performance is not high, real-time poor. Platform: |
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Author:阳关 |
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Description: 虽然粒子滤波算法可以作为解决SLAM问题有效手段，但是该算法仍然存在着一些问题其中最主要的问题是需要用大量的样本数量能很好地近似系统的后验概率密度。-Although the particle filter can be used as an effective means to solve the SLAM problem, but the algorithm still exist some problems in which the most important issue is the need for a large number of sample size with a good approximation to the system a posteriori probability density. Platform: |
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Author:乜一 |
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Description: 虽然粒子滤波算法可以作为解决SLAM问题的有效手段，但是该算法仍然存在着一些问题。其中最主要的问题是需要用大量的样本数量才能很好地近似系统的后验概率密度。-Although the particle filter to solve the SLAM problem can be an effective means, but the algorithm still exist some problems. One of the most important issue is the need for a large number of samples with the number of systems can be a good approximation of the posterior probability density. Platform: |
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Author:乜一 |
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Description: 虽然粒子滤波算法可以作为解决SLAM问题的有效手段，但是该算法仍然存在着一些问题。其中最主要的问题是需要用大量的样本数量才能很好地近似系统的后验概率密度。-Although the particle filter to solve the SLAM problem can be an effective means, but the algorithm still exist some problems. One of the most important issue is the need for a large number of samples with the number of systems can be a good approximation of the posterior probability density. Platform: |
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Author:乜一 |
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Description: 虽然粒子滤波算法可以作为解决SLAM问题的有效手段，但是该算法仍然存在着一些问题。其中最主要的问题是需要用大量的样本数量才能很好地近似系统的后验概率密度。-Although the particle filter to solve the SLAM problem can be an effective means, but the algorithm still exist some problems. One of the most important issue is the need for a large number of samples with the number of systems can be a good approximation of the posterior probability density. Platform: |
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Author:乜一 |
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Description: 本程序已被本人整理到WORD文档中，编程语言为MATLAB，本文设计的滤波器采用傅里叶级数展开法。模拟的杂波的功率谱密度采用BVURG法，概率密度函数的估计采用直方图估计法，设计参数皆在文档中表明。此程序已经验证是正确可执行的，并能生成图形，值得下载！-This program has been organized into WORD document I, the programming language MATLAB, the filter designed in this paper Fourier series expansion method. Simulated clutter power spectral density using BVURG method, the probability density function is estimated using histogram estimation methods, design parameters are indicated in the document. This procedure has been verified is correct executable, and can generate the graph, it is worth to download! Platform: |
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Author:biggirl |
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Description: 粒子滤波(PF: Particle Filter)的思想基于蒙特卡洛方法(Monte Carlo methods)，它是利用粒子集来表示概率，可以用在任何形式的状态空间模型上。其核心思想是通过从后验概率中抽取的随机状态粒子来表达其分布，是一种顺序重要性采样法(Sequential Importance Sampling)。简单来说，粒子滤波法是指通过寻找一组在状态空间传播的随机样本对概率密度函数 进行近似，以样本均值代替积分运算，从而获得状态最小方差分布的过程。这里的样本即指粒子,当样本数量N→∝时可以逼近任何形式的概率密度分布。-Particle filter (PF: Particle Filter) ideas based on Monte Carlo methods (Monte Carlo methods), which is set to represent the probability of a particle, can be used in any form of state space model. The core idea is to extract from the posterior probability of the random state of particle to express the distribution is a sequential importance sampling method (Sequential Importance Sampling). In short, particle filtering method is by looking for a spread in state space probability density function of random samples to approximate to the sample mean instead of integral operators to gain distribution in the state minimum variance process. Here' s the sample i.e. particles, when the sample size N → α can approach any form of probability density distribution. Platform: |
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Author:fanlianxiang |
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Description: 主要对扩展卡尔曼滤波（EKF）、无迹卡尔曼滤波（UKF）及改进无迹卡尔曼滤波（MAUKF）算法进行研究，研究了三种算法的基本原理和各自的特点。其中扩展卡尔曼滤波器是将卡尔曼滤波器局部线性化，其算法简单，计算量小，适用于弱非线性、高斯环境。无迹卡尔曼滤波器是用一系列确定样本来逼近状态的后验概率密度。改进无迹卡尔曼滤波算法在UKF的基础上引入衰减因子。-The thesis focuses on the extended Kalman filter (EKF), unscented Kalman filter (UKF) and the improvement of unscented Kalman filter (MAUKF) algorithm research, researched the basic principles of three algorithms and their characteristics. Extended Kalman filter (EKF) is the local linear Kalman filter, the algorithm and computation are simple in weakly nonlinear and Gaussian environment. Unscented Kalman filter (UKF) is determined using a series of samples to approximate the state posterior probability density. Improved UKF algorithm (MAUKF) is introduced attenuation factor based on the UKF. Platform: |
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Author:zyz |
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Description: 无迹卡尔曼滤波UKF是重要的非线性滤波方法。它采用UT变换的方法，不再近似系统的非线性方程，它仍然用高斯随机变量表示状态分布，不过是用特定选择的样本点加以描述，每个点叫一个高斯点，它从系统状态的概率密度函数中取出；然后，按系统的真实模型演化，得到非线性演化后的σ点，使得样本均值和样本方差是真实均值和真实方差的好的近似。
在这个程序中，实现了基于UKF的滤波方法，并且建立了两种仿真环境进行实验。-Unscented Kalman filter UKF is an important nonlinear filtering method. It uses the UT transformation method, no similar system of nonlinear equations, it still says the state with the Gaussian distribution of random variables, but the specific choice is to describe the sample points, each point is called a Gaussian point, it is from probability density function of the system state to remove then, according to the true model of evolution of the system obtained after the nonlinear evolution of σ point, making the sample mean and sample variance is true variance of the mean and the true good approximation.
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Author:xiaoyu |
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Description: Mahler发表的概率假设密度滤波和随机集领域的开创性文章-It is presened by Prof.Mahler for the probability hypothesis density filter and finite random set Platform: |
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Author:xiaohao |
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Description: IEEE trans. 发表的高斯概率假设密度滤波的开创性文章-It is published in an IEEE tans. on Gaussian mixture probability hypothesis density filter and finite random set Platform: |
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Author:xiaohao |
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Description: 粒子滤波实现的概率假设密度滤波和随机集领域的开创性文章，发表于IEEE trans-It is presened on the particle filter for the probability hypothesis density filter and finite random set Platform: |
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Author:xiaohao |
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Description: IEEE trans上发表的高斯混合概率假设密度滤波的证明性论文-It is presened for the GM
probability hypothesis density filter and finite random set Platform: |
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Author:xiaohao |
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Description: 该文提出一种基带GMSK 信号相关器，并从GMSK 解调信号的相位概率分布函数以及独立同分布随机变量和的概率分布函数出发，给出了该相关器的自相关峰和互相关峰的概率分布函数。从概率论与数理统计出发，
推导了一种统一的数字相关器(数字匹配滤波器)的自相关峰和互相关峰的概率分布函数。数值计算结果表明，在相同条件下，该基带GMSK 信号相关器的误检概率比数字相关器的低近一个数量级。-A type of baseband GMSK signal correlator is presented in this paper. From the phase probability
density function of demodulated GMSK signal and the probability distribution function of the sums of independent
identical distribution random variables, the auto-correlation and cross-correlation functions of the correlator are
proposed. A uniform auto-correlation and cross-correlation probability distribution functions of digital
correlator(digital matched filter) is derived from the probability and statistics theory. Numerical results
demonstrate that the error probability of the baseband GMSK signal correlator is about one order of magnitude
lower than that of digital one under the same condition. Platform: |
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Author:li |
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Description: The Gaussian mixture probability hypothesis density filter (GM-PHD Filter) was proposed recently for jointly estimating the time-varying number of targets and their states a noisy sequence of sets of measurements. - The Gaussian mixture probability hypothesis density filter (GM-PHD Filter) was proposed recently for jointly estimating the time-varying number of targets and their states a noisy sequence of sets of measurements . Platform: |
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Author:石鸿逸 |
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