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

页码：823 / 828

页数：6

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