Particle estimation algorithm using angle between observation vectors for nonlinear system state

Jun Liang; Yu Peng; Xiyuan Peng

Particle estimation algorithm using angle between observation vectors for nonlinear system state

Jun Liang^*
, Yu Peng
, Xiyuan Peng

^*Corresponding author for this work

School of Electronics and Information Engineering

Harbin Institute of Technology

Research output: Contribution to journal › Article › peer-review

Abstract

A particle estimation algorithm where the weight of the particle is related to angle between observation vectors is presented for nonliear system state. When the likelihood has a bimodal nature, this algorithm leads to more accurate state estimates than Sequential importance resampling (SIR), Auxiliary particle filter (APF), Regularized particle filter (RPF), and Gaussian particle filter (GPF).

Original language	English
Pages (from-to)	700-702
Number of pages	3
Journal	Chinese Journal of Electronics
Volume	18
Issue number	4
State	Published - Oct 2009

Keywords

Auxiliary particle filter (APF)
Gaussian particle filter (GPF)
Nonliear system
Particle alter
Regularized particle filter (RPF)
Sequential importance resampling (SIR)
State estimation

Cite this

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title = "Particle estimation algorithm using angle between observation vectors for nonlinear system state",

abstract = "A particle estimation algorithm where the weight of the particle is related to angle between observation vectors is presented for nonliear system state. When the likelihood has a bimodal nature, this algorithm leads to more accurate state estimates than Sequential importance resampling (SIR), Auxiliary particle filter (APF), Regularized particle filter (RPF), and Gaussian particle filter (GPF).",

keywords = "Auxiliary particle filter (APF), Gaussian particle filter (GPF), Nonliear system, Particle alter, Regularized particle filter (RPF), Sequential importance resampling (SIR), State estimation",

author = "Jun Liang and Yu Peng and Xiyuan Peng",

year = "2009",

month = oct,

language = "英语",

volume = "18",

pages = "700--702",

journal = "Chinese Journal of Electronics",

issn = "1022-4653",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

number = "4",

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T1 - Particle estimation algorithm using angle between observation vectors for nonlinear system state

AU - Liang, Jun

AU - Peng, Yu

AU - Peng, Xiyuan

PY - 2009/10

Y1 - 2009/10

N2 - A particle estimation algorithm where the weight of the particle is related to angle between observation vectors is presented for nonliear system state. When the likelihood has a bimodal nature, this algorithm leads to more accurate state estimates than Sequential importance resampling (SIR), Auxiliary particle filter (APF), Regularized particle filter (RPF), and Gaussian particle filter (GPF).

AB - A particle estimation algorithm where the weight of the particle is related to angle between observation vectors is presented for nonliear system state. When the likelihood has a bimodal nature, this algorithm leads to more accurate state estimates than Sequential importance resampling (SIR), Auxiliary particle filter (APF), Regularized particle filter (RPF), and Gaussian particle filter (GPF).

KW - Auxiliary particle filter (APF)

KW - Gaussian particle filter (GPF)

KW - Nonliear system

KW - Particle alter

KW - Regularized particle filter (RPF)

KW - Sequential importance resampling (SIR)

KW - State estimation

UR - https://www.scopus.com/pages/publications/70449366396

M3 - 文章

AN - SCOPUS:70449366396

SN - 1022-4653

VL - 18

SP - 700

EP - 702

JO - Chinese Journal of Electronics

JF - Chinese Journal of Electronics

IS - 4

ER -

Particle estimation algorithm using angle between observation vectors for nonlinear system state

Abstract

Keywords

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