卡尔曼滤波器
计算机科学
分歧(语言学)
α-β滤光片
控制理论(社会学)
快速卡尔曼滤波
维数(图论)
不变扩展卡尔曼滤波器
扩展卡尔曼滤波器
滤波器(信号处理)
状态空间表示
集合卡尔曼滤波器
算法
状态空间
基础(线性代数)
噪音(视频)
数学
人工智能
计算机视觉
移动视界估计
几何学
统计
图像(数学)
哲学
语言学
纯数学
控制(管理)
作者
Wei Fan,Zupei Zhang,Kunpeng Jia
出处
期刊:Journal of physics
[IOP Publishing]
日期:2021-08-01
卷期号:2005 (1): 012005-012005
被引量:2
标识
DOI:10.1088/1742-6596/2005/1/012005
摘要
Abstract Kalman filter processes the input and observation signals with noise on the basis of linear state space representation to obtain the system state or real signal. In the one-dimensional model, due to the lack of multi-dimensional description of the target, the prior estimate of the state is often the measured value of the previous moment. When the target state changes, the divergence phenomenon will occur. Aiming at the problem that the one-dimensional traditional Kalman filter lacks the target observation dimension, which leads to the divergence or imprecision of the filter, this paper focuses on improving the estimation method of the target state, and proposes a real-time prediction model based on the cascade structure. This model can improve the response of Kalman filter to the change of target state and dynamically adjust the Kalman iterative domain to improve the measurement accuracy. The digital signal filtering simulation is carried out and the performance of the filter is verified based on LabVIEW. Experimental results show that the algorithm can maintain the accuracy and real-time performance of filtering when only one dimension observation results are obtained.
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