A suboptimal algorithm for the optimal Bayesian filter using receding horizon FIR filter

被引：0
|
作者：
Kim, YS [1 ]
Choi, SL [1 ]
Hong, KS [1 ]
机构：
[1] Pusan Natl Univ, Dept Mech & Intelligent Syst, Kumjeong Ku, Pusan 609735, South Korea
来源：
ISIE 2001: IEEE INTERNATIONAL SYMPOSIUM ON INDUSTRIAL ELECTRONICS PROCEEDINGS, VOLS I-III | 2001年
关键词：
D O I：
暂无
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
The optimal Bayesian filter (OBF) for a single target is known to provide best tracking performance in a cluttered environment. However, the problem of its memory and computation requirements increases with time. In this paper, the inevitable problem of the OBF of Singer et al.[7] is resolved by using a suboptimal algorithm. The suboptimal algorithm is derived by using only measurements in the receding horizon interval. With the assumptions that the system and observation transition matrices are observable and the horizon interval length is bounded by the dimension of the system, the unbiased property is satisfied.
引用
收藏
页码：1860 / 1865
页数：6
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