递推的贝叶斯估计方法 |
| |
引用本文: | 宁永成,侯代文.递推的贝叶斯估计方法[J].四川兵工学报,2013(10):130-136. |
| |
作者姓名: | 宁永成 侯代文 |
| |
作者单位: | 91439部队460所,辽宁大连116041 |
| |
摘 要: | 对贝叶斯估计的原理及应用进行了综述,在系统阐述贝叶斯估计理论的基础上,按照对后验概率密度函数表示方式的不同,分析和总结了隐马尔可夫模型、卡尔曼滤波、分布拟合滤波以及粒子滤波等算法的特点、使用方法和使用范围;最后,对贝叶斯估计的发展方向进行了展望.
|
关 键 词: | 贝叶斯估计 隐马尔可夫模型 卡尔曼滤波 分布拟合 粒子滤波 |
A Survey of Recursive Bayesian Estimation Methods |
| |
Authors: | NING Yong-cheng HOU Dai-wen |
| |
Affiliation: | (Institute 460 of Unit 91439, PLA, Liaoning 116041, China) |
| |
Abstract: | The theory and applications related to sequential Bayesian estimation were surveyed. Various estimating algorithms, such as the Hidden Markov Model, the Kalman Filter, the Assumed-density Filter and the Particle Filter were analyzed and summarized according to the way their posterior density function are expressed. Finally, further research directions are pointed out. |
| |
Keywords: | sequential Bayesian estimation hidden Markov model Kalman filter assumed-density filter particle filter |
本文献已被 维普 等数据库收录! |
|