Description: 基于次优贝叶斯估计的非线形非高斯条件下的粒子滤波器的MATELAB仿真-based Bayesian estimation of non-linear non-Gaussian under the conditions of the particle filter simulation MATELAB Platform: |
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Author:husheng |
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Description: 工学博士学位论文
目前，扩展卡尔曼滤波是研究初始对准和惯性/GPS组合导航问题的一个主要手段。
但初始对准和惯性/GPS组合导航问题本质上是非线性的，对模型进行线性化的扩展卡
尔曼滤波在一定程度上影响了系统的性能。近年来，直接使用非线性模型的
UKF(Unscented Kalman Filtering, UKF)和粒子滤波，正在逐渐成为研究非线性估计问题
的热点和有效方法。
本文研究了UKF和粒子滤波两种非线性滤波方法，并将其应用于非线性静基座对
准和惯性/GPS组合导航，系统地研究了初始对准和惯性/GPS组合导航中各种非线性项-Engineering PhD thesis Currently, EKF is the initial alignment study and inertial / GPS navigation of a major means. However, initial alignment and inertial / GPS navigation on the nature of the problem is nonlinear. on the model of linear expansion of the Kalman filter certain extent affected the performance of the system. In recent years, direct use of the non-linear model (UKF Unscented Kalman Filtering. UKF) and the particle filter, is gradually becoming nonlinear estimation of the hot and effective method. This paper studies the UKF and particle filter both nonlinear filtering method, will be applied to nonlinear static Base Alignment and inertial / GPS navigation, systematic study of initial alignment and inertial / GPS navigation various nonlinear term Platform: |
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Author:daniel |
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Description: a copylefted C implementation of a technique for non-linear, robust
homography estimation from matched image point features. Platform: |
Size: 47937 |
Author:sjtuzyk |
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Description: homest is a C/C++ implementation of an algorithm for non-linear, robust homography estimation from matched image point features that is distributed under the GNU General Public License. Platform: |
Size: 51826 |
Author:Doffery |
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Description: 一篇IEEE在06年的经典文献,是关于非线性估计方法在图象恢复中的理论方法.太经典了.请大家一起参阅-IEEE in 2006 one of the classic literature, is about the non-linear estimation method in the theory of image restoration methods. A classic too. Please refer to U.S. together Platform: |
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Author:单昊 |
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Description: 小波用于函数估计的一些好文章，包括固定点设计的情况，还有非线性的估计-Wavelet function is estimated for a number of good articles, including the case of fixed-point design, there are non-linear estimation Platform: |
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Author:林翠香 |
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Description: 这是关于TOA的一种抑制 NLOS传播非线性最小二乘法 TOA校正因子估算法-This is on the TOA of a NLOS inhibit the spread of non-linear least squares estimation method TOA correction factor Platform: |
Size: 142336 |
Author:展琳琳 |
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Description: EKF_PF 基于扩展kalman的粒子滤波 可解决非线性状态估计问题-EKF_PF based on extended kalman particle filter to address the issue of non-linear state estimation Platform: |
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Author:fortune |
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Description: 1stOpt 是七维高科有限公司（7D-Soft High Technology Inc.）独立开发，
拥有完全自主知识产权的一套数学优化分析综合工具软件包。在非线性回归，曲线拟合，非线性复杂模型参数估算求解，线性/非线性规划等领域首屈一指；
1stOpt 应用范围
1) 模型自动优化率定
2) 参数估算
3) 任意模型公式线性，非线性拟合，回归
4) 非线性连立方程组求解
5) 任意维函数，隐函数极值求解
6) 隐函数根求解，作图，求极值
7) 线性，非线性及整数规划
8) 组合优化问题
9) 高级计算器
附带指导文件-1stOpt Hi-Tech Co., Ltd. is one of the seven-dimensional (7D-Soft High Technology Inc.) Independently developed, have complete independent intellectual property rights of a set of integrated tools for analysis of mathematical optimization software package. In the non-linear regression, curve fitting, non-linear model parameters to estimate the complexity of solving linear/nonlinear programming in areas such as second to none 1stOpt scope of application 1) model to automatically optimize rate 2) parameter estimation 3) any model of the formula line sexual, non-linear fitting, regression 4) non-linear simultaneous equations to solve even 5) arbitrary-dimensional function, implicit function solving extremum 6) implicit function solving the root, mapping, and extreme value 7) of linear, non-linear and integer programming 8) combinatorial optimization problem 9) high-level guidance document attached calculator Platform: |
Size: 7420928 |
Author:zhangqi |
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Description: 在实时平台上,高斯混合模型(GMM)具有计算有效性和易于实现的优点。最大似然规则中,模型参数不
断更新,但由于爬山特征,任意的原始模型参数估计通常将导致局部最优 遗传算法(GA)适于求解复杂组合优化问
题及非线性函数优化。提出了基于说话人识别的可以解决GMM局部最优问题的GMM/GA新算法,实验结果表明,
提出的GMM/GA新算法比纯粹的GMM算法能获得更优的效果。
- In real-time platform, the Gaussian mixture model (GMM) with the calculation of the effectiveness and easy to realize benefits. Maximum likelihood rule, the model parameters are not
Broken updates, but due to climbing features, any of the original model parameter estimation will usually result in local optimum genetic algorithm (GA) is suitable for solving complex combinatorial optimization question
Title and non-linear function optimization. Proposed speaker recognition based on GMM can solve the problem of local optimal GMM/GA new algorithm, experimental results show that the
Proposed GMM/GA new algorithm than purely GMM algorithm can get better results. Platform: |
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Author:于高 |
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Description: 粒子滤波算法；粒子滤波算法源于Montecarlo的思想，即以某事件出现的频率来指代该事件的概率。因此在滤波过程中，需要用到概率如P(x)的地方，一概对变量x采样，以大量采样的分布近似来表示P(x)。因此，采用此一思想，在滤波过程中粒子滤波可以处理任意形式的概率，而不像Kalman滤波只能处理高斯分布的概率问题。他的一大优势也在于此。-these codes are particle filter resources codes which solve non-linear estimation problems.I wish that it is helpful to some people.I am glad to share it with others.
Platform: |
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Author:lixiangyang |
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Description: 此代码是用C++写的计算平面单应的程序，利用到了LM优化，对于计算机视觉研究人员应该很有用-his is homest, a copylefted C implementation of a technique for non-linear, robust
homography estimation from matched image point features Platform: |
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Author:吴文欢 |
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Description: age. However, GAUSS is not appropriate for, say, writing a menu system a
general-purpose language is probably easier. Nor is GAUSS appropriate for standard applications
on standard datasets. There is little point in writing a probit estimation routine in GAUSS for a
small dataset. Firstly, there are already routines commercially available for non-linear estimation
using GAUSS. More importantly, TSP, LimDep, etc will already perform the estimation and there
is no necessity to learn anything at all about GAUSS to use these programs. However, to get extra
speciÖ cation tests, for example, a straightforward solution would be to code a routine and amend
the preexisting GAUSS probit program to call the new proced-age. However, GAUSS is not appropriate for, say, writing a menu system a
general-purpose language is probably easier. Nor is GAUSS appropriate for standard applications
on standard datasets. There is little point in writing a probit estimation routine in GAUSS for a
small dataset. Firstly, there are already routines commercially available for non-linear estimation
using GAUSS. More importantly, TSP, LimDep, etc will already perform the estimation and there
is no necessity to learn anything at all about GAUSS to use these programs. However, to get extra
speciÖ cation tests, for example, a straightforward solution would be to code a routine and amend
the preexisting GAUSS probit program to call the new proced Platform: |
Size: 609280 |
Author:fahad |
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Search - non linear estimation