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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
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％ PURPOSE : Demonstrate the differences between the following filters on the same problem:
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％ 1) Extended Kalman Filter (EKF)
％ 2) Unscented Kalman Filter (UKF)
％ 3) Particle Filter (PF)
％ 4) PF with EKF proposal (PFEKF)
％ 5) PF wit
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The algorithms are coded in a way that makes it trivial to apply them to other problems. Several generic routines for resampling are provided. The derivation and details are presented in: Rudolph van der Merwe, Arnaud Doucet, Nando de Freitas and Eri
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详细的unscented particle filter 程序
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％ PURPOSE : Demonstrate the differences between the following filters on the same problem:
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％ 1) Extended Kalman Filter (EKF)
％ 2) Unscented Kalman Filter (UKF)
％ 3) Particle Filter (PF)
％ 4) PF with EKF proposal (PFEKF)
％ 5) PF with UK
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关于粒子滤波的仿真程序,比较了粒子滤波和卡尔曼滤波的优缺点,the unscented particle filtering
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里面包含kalman，扩展kalman，无迹kalman，粒子滤波，无迹粒子滤波等源码的实现。-upf--The Unscented Particle Filter
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该程序包实现的是无味(Unscented)粒子滤波算法，与一般粒子滤波的不同处是：采用UKF近似粒子滤波的建议分布函数。-The package to achieve the tasteless (Unscented) particle filter, particle filter in general the differences are: use of UKF approximate particle filter proposal distribution function.
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关于粒子滤波的仿真程序,比较了粒子滤波和卡尔曼滤波的优缺点-the unscented particle filtering
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BAYESIAN FILTERING: including KF, EKF, Unscented EKF, Particle Filter & etc
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粒子滤波、无迹粒子滤波算法程序，高斯混合模型参数设置等详细代码-Particle filter, unscented particle filter program, Gaussian mixture model parameter settings, and more code
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一个实现简单的无机粒子滤波matlab程序，用于学习交流-A simple unscented particle filter matlab program for learning exchanges
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无迹粒子滤波例程，简单的跟踪应用。无杂波环境下的-Unscented particle filter routine, a simple tracking applications
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This is unscented kalman filter and particle filter matlab algorithms.
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该代码用于实现粒子滤波视觉目标跟踪(PF)、卡尔曼粒子滤波视觉目标跟踪(KPF)、无迹粒子滤波视觉目标跟踪(UPF)。它们是本人这两年来编写的核心代码，用于实现鲁棒的视觉目标跟踪，其鲁棒性远远超越MeanShift(均值转移)和Camshift之类。用于实现视觉目标跟踪的KPF和UPF都是本人花费精力完成，大家在网上是找不到相关代码的。这些代码虽然只做了部分代码优化，但其优化版本已经成功应用于我们研究组研发的主动视觉目标跟踪打击平台中。现在把它们奉献给大家！-These codes are us
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无香粒子滤波的一个matlab例程，其中有ekf,ukf,pf,upf-In these demos, we demonstrate the use of the extended Kalman filter (EKF), unscented Kalman filter (UKF), standard particle filter (a.k.a. condensation, survival of the fittest, bootstrap filter, SIR, sequential M
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Unscented Particle Filter源程序-Unscented Particle Filter PROGRAM
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完整的Kalman Filter、EKF、IEKF、Unscented Kalman Filter及Particle Filter滤波程序。-The complete Kalman Filter, EKF, IEKF, Unscented Kalman Filter and Particle Filter filtering procedure.
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融合残差Unscented粒子滤波和区别性稀疏表示的鲁棒目标跟踪-Robust target fusion residual Unscented particle filter and distinction of the sparse representation of tracking
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Nando de Freitas' sequential Monte Carlo demos in Matlab. Unscented Particle Filter.
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