Comparison of Auxiliary and Likelihood Particle Filters for State Estimation of Dynamical Systems

被引：0

作者：

Michalski, Jacek ^{[1
]}

Kozierski, Piotr ^{[1
,2
]}

Zietkiewicz, Joanna ^{[1
]}

机构：

[1] Poznan Univ Tech, Fac Elect Engn, Inst Control Robot & Informat Engn, Div Control & Robot, Piotrowo 3a St, PL-60965 Poznan, Poland

[2] Poznan Univ Tech, Fac Comp, Inst Automat & Robot, Div Elect Syst & Signal Proc, Piotrowo 3a St, PL-60965 Poznan, Poland

来源：

PRZEGLAD ELEKTROTECHNICZNY | 2018年 / 94卷 / 12期

关键词：

particle filters; state estimation; dynamical systems; Kalman filters; nonlinear plants;

D O I：

10.15199/48.2018.12.19

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

In this paper, algorithms of the state estimation of dynamical systems, using different types of particle filters, have been presented. Three Particle Filter methods have been used: Bootstrap Filter, Auxiliary Particle Filter and Likelihood Particle Filter. These methods have been applied to two nonlinear objects, with quadratic measurement functions. The results have been additionally compared with the outcome from Kalman filters. Based on the obtained results (5 different quality indices) the estimation methods have been evaluated.

引用

页码：86 / 90

页数：5

共 24 条

[1]

Abur A., 2004, POWER SYSTEM STATE E, P17, DOI [10.1201/9780203913673.ch2, DOI 10.1201/9780203913673.CH2]

[2]

[Anonymous], 2004, KALMAN EXTENDED KALM

[3] A tutorial on particle filters for online nonlinear/non-Gaussian Bayesian tracking [J].

Arulampalam, MS ;

Maskell, S ;

Gordon, N ;

Clapp, T .

IEEE TRANSACTIONS ON SIGNAL PROCESSING, 2002, 50 (02) :174-188

[4]

Candy JV., 2009, BAYESIAN SIGNAL PROC, P36, DOI [10.1002/9780470430583, DOI 10.1002/9780470430583]

[5] Power System Dynamic State Estimation Based on a New Particle Filter [J].

Chen Huanyuan ;

Liu Xindong ;

She Caiqi ;

Yao Cheng .

2011 2ND INTERNATIONAL CONFERENCE ON CHALLENGES IN ENVIRONMENTAL SCIENCE AND COMPUTER ENGINEERING (CESCE 2011), VOL 11, PT B, 2011, 11 :655-661

[6]

Doucet A., 2009, HDB NONLINEAR FILTER, VVolume 12, P656

[7]

Florek A., 2015, DYNAMIC SIGNALS SYST

[8] NOVEL-APPROACH TO NONLINEAR NON-GAUSSIAN BAYESIAN STATE ESTIMATION [J].

GORDON, NJ ;

SALMOND, DJ ;

SMITH, AFM .

IEE PROCEEDINGS-F RADAR AND SIGNAL PROCESSING, 1993, 140 (02) :107-113

[9] Robust Adaptive Kalman Filter for estimation of UAV dynamics in the presence of sensor/actuator faults [J].

Hajiyev, Chingiz ;

Soken, Haul Ersin .

AEROSPACE SCIENCE AND TECHNOLOGY, 2013, 28 (01) :376-383

[10]

Kalman, 1960, J BASIC ENG-T ASME, V82, DOI [DOI 10.1115/1.3662552, 10.1115/1.3662552]

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