An Ensemble Kushner-Stratonovich-Poisson Filter for Recursive Estimation in Nonlinear Dynamical Systems

被引：3
|
作者：
Venugopal, Mamatha [1 ]
Vasu, Ram Mohan [1 ]
Roy, Debasish [2 ]
机构：
[1] Indian Inst Sci, Dept Instrumentat & Appl Phys, Opt Tomog Lab, Bangalore 560012, Karnataka, India
[2] Indian Inst Sci, Dept Civil Engn, Computat Mech Lab, Bangalore 560012, Karnataka, India
来源：
IEEE TRANSACTIONS ON AUTOMATIC CONTROL | 2016年 / 61卷 / 03期
关键词：
Automatic control; Bayes method; filtering; Monte Carlo methods; Poisson processes; recursive estimation; MONTE-CARLO METHODS; PARTICLE;
D O I：
10.1109/TAC.2015.2450113
中图分类号：
TP [自动化技术、计算机技术];
学科分类号：
0812 ;
摘要：
We propose a Monte Carlo filter for recursive estimation of diffusive processes that modulate the instantaneous rates of Poisson measurements. A key aspect is the additive update, through a gain-like correction term, empirically approximated from the innovation integral in the time-discretized Kushner-Stratonovich equation. The additive filter-update scheme eliminates the problem of particle collapse encountered in many conventional particle filters. Through a few numerical demonstrations, the versatility of the proposed filter is brought forth.
引用
收藏
页码：823 / 828
页数：6
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