控制理论(社会学)
卡尔曼滤波器
地平线
数学
不变扩展卡尔曼滤波器
α-β滤光片
扩展卡尔曼滤波器
滤波器(信号处理)
计算机科学
移动视界估计
人工智能
统计
控制(管理)
几何学
计算机视觉
作者
Wook Hyun Кwon,Pyung Soo Kim,PooGyeon Park
摘要
A receding horizon Kalman FIR filter is presented that combines the Kalman filter and the receding horizon strategy when the horizon initial state is assumed to be unknown. The suggested filter is a FIR filter form which has many good inherent properties. It can always be defined irrespective of singularity problems caused by unknown information about the horizon initial state. The suggested filter can be represented in either an iterative form or a standard FIR form. It is also shown that the suggested filter possesses the unbiasedness property and the remarkable deadbeat property irrespective of any horizon initial condition. The validity of the suggested filter is illustrated by numerical examples.
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