图 书 馆
Σ最优滤波中的随机微分方程的Σ点和近似粒子Sigma point and particle approximations of stochastic differential equations in optimal filtering |
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| 课程网址: | http://videolectures.net/aispds08_sarkka_sppa/ |
| 主讲教师: | Simo Särkkä |
| 开课单位: | 赫尔辛基大学 |
| 开课时间: | 2008-08-05 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | The unscented transform (UT) is a relatively recent method for approximating non-linear transformations of random variables. Instead of the classical Taylor series approximations, it is based on forming a set of sigma points, which are propagated through the non-linearity. The unscented Kalman filter (UKF) is an alternative to the extended Kalman filter (EKF), which utilizes the unscented transform in the filter computations. However, in its original form, the UKF is a discrete-time algorithm and it cannot be directly applied to estimation problems, where the state dynamics are modeled in continuous-time as stochastic differential equations. In the talk I will review the Taylor series, sigma-point (unscented) and particle approximations of stochastic differential equations in optimal (Bayesian) filtering context and present some applications of the methods in navigation systems and in monitoring of chemical processes. |
| 关 键 词: | 非线性变换; 无色卡尔曼滤波器; 随机微分方 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-01-16：chenxin |
| 阅读次数: | 52 |