A new particle filter based on smooth variable structure filter

Haomiao Zhou; Yuanqing Xia; Yulin Deng

doi:10.1002/acs.3067

A new particle filter based on smooth variable structure filter

Haomiao Zhou, Yuanqing Xia^*, Yulin Deng

^*Corresponding author for this work

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

6 Citations (Scopus)

Abstract

In this paper, a new particle filter (PF) based on the smooth variable structure filter (SVSF) is presented. Here, PF is a popular estimation method that makes use of distributed particles to form an approximation of the probability distribution function. The SVSF is a sliding mode estimator, which is based on predictor-corrector methods and sliding mode concepts. Moreover, the gain of the SVSF is calculated based on a switching surface. The new filter called SVSF-PF utilizes a smoothing boundary layer of SVSF to control the particles in a range, which can improve the overall estimation accuracy and stability. Finally, a simulation example of nonlinear target tracking is used to demonstrate effectiveness.

Original language	English
Pages (from-to)	32-41
Number of pages	10
Journal	International Journal of Adaptive Control and Signal Processing
Volume	34
Issue number	1
DOIs	https://doi.org/10.1002/acs.3067
Publication status	Published - 1 Jan 2020

Keywords

chattering
particle filter
smooth variable structure filter

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10.1002/acs.3067

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title = "A new particle filter based on smooth variable structure filter",

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A new particle filter based on smooth variable structure filter

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