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。
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.
出处
《四川兵工学报》
CAS
2013年第10期130-136,共7页
Journal of Sichuan Ordnance
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
sequential Bayesian estimation
hidden Markov model
Kalman filter
assumed-density filter
particle filter
