颗粒过滤器
计算机科学
卡尔曼滤波器
马尔科夫蒙特卡洛
信号处理
马尔可夫链
无线
状态空间
马尔可夫过程
贝叶斯概率
实时计算
分布式计算
算法
人工智能
电信
机器学习
统计
雷达
数学
作者
Fayan Zhu,Linmei Wang,Jiahong Wen,Zunxin Zheng
出处
期刊:IEEE Network
[Institute of Electrical and Electronics Engineers]
日期:2019-07-01
卷期号:33 (4): 42-47
标识
DOI:10.1109/mnet.2019.1800432
摘要
Filtering technology has been widely applied in signal processing and object tracking for decades, and most recently it has become a useful instrument for time series analysis. In this article, we introduce several popular technologies developed for analysis of the dynamic state space model, including Kalman and particle filtering, Markov chain Monte Carlo algorithms, as well as the sequential Bayesian learning method. Their applications in fields of interest are also discussed. Filtering technologies have great superiority in solving the problems arising from management and communications, making them deserving of further exploration.
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