A Maximum Correntropy Divided Difference Filter for Cooperative Localization

Chengjiao Sun; Yonggang Zhang; Guoqing Wang; Wei Gao

doi:10.1109/ACCESS.2018.2859391

A Maximum Correntropy Divided Difference Filter for Cooperative Localization

Chengjiao Sun
, Yonggang Zhang^*
, Guoqing Wang
, Wei Gao

^*Corresponding author for this work

Harbin Engineering University
School of Electrical Engineering and Automation, Harbin Institute of Technology

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

31 Scopus citations

Abstract

This paper derives a new maximum correntropy divided difference filter (DDF) to address the heavy-tailed measurement noise induced by non-Gaussian measurements in cooperative localization of autonomous underwater vehicles. By integrating the advantages of both the DDF and the maximum correntropy criterion, the proposed filter exhibits localization accuracy and robustness to address the heavy-tailed impulsive noise. The proposed maximum correntropy DDF has been tested through a lake trial. Experimental results indicate the superior performance of the proposed algorithm.

Original language	English
Article number	8419783
Pages (from-to)	41720-41727
Number of pages	8
Journal	IEEE Access
Volume	6
DOIs	https://doi.org/10.1109/ACCESS.2018.2859391
State	Published - 25 Jul 2018
Externally published	Yes

Keywords

Maximum correntropy
autonomous underwater vehicles
cooperative localization
divided difference filter
nonlinear systems
outliers

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10.1109/ACCESS.2018.2859391

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title = "A Maximum Correntropy Divided Difference Filter for Cooperative Localization",

abstract = "This paper derives a new maximum correntropy divided difference filter (DDF) to address the heavy-tailed measurement noise induced by non-Gaussian measurements in cooperative localization of autonomous underwater vehicles. By integrating the advantages of both the DDF and the maximum correntropy criterion, the proposed filter exhibits localization accuracy and robustness to address the heavy-tailed impulsive noise. The proposed maximum correntropy DDF has been tested through a lake trial. Experimental results indicate the superior performance of the proposed algorithm.",

keywords = "Maximum correntropy, autonomous underwater vehicles, cooperative localization, divided difference filter, nonlinear systems, outliers",

author = "Chengjiao Sun and Yonggang Zhang and Guoqing Wang and Wei Gao",

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year = "2018",

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AU - Sun, Chengjiao

AU - Zhang, Yonggang

AU - Wang, Guoqing

AU - Gao, Wei

PY - 2018/7/25

Y1 - 2018/7/25

N2 - This paper derives a new maximum correntropy divided difference filter (DDF) to address the heavy-tailed measurement noise induced by non-Gaussian measurements in cooperative localization of autonomous underwater vehicles. By integrating the advantages of both the DDF and the maximum correntropy criterion, the proposed filter exhibits localization accuracy and robustness to address the heavy-tailed impulsive noise. The proposed maximum correntropy DDF has been tested through a lake trial. Experimental results indicate the superior performance of the proposed algorithm.

AB - This paper derives a new maximum correntropy divided difference filter (DDF) to address the heavy-tailed measurement noise induced by non-Gaussian measurements in cooperative localization of autonomous underwater vehicles. By integrating the advantages of both the DDF and the maximum correntropy criterion, the proposed filter exhibits localization accuracy and robustness to address the heavy-tailed impulsive noise. The proposed maximum correntropy DDF has been tested through a lake trial. Experimental results indicate the superior performance of the proposed algorithm.

KW - Maximum correntropy

KW - autonomous underwater vehicles

KW - cooperative localization

KW - divided difference filter

KW - nonlinear systems

KW - outliers

UR - https://www.scopus.com/pages/publications/85050591225

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DO - 10.1109/ACCESS.2018.2859391

M3 - 文章

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SP - 41720

EP - 41727

JO - IEEE Access

JF - IEEE Access

M1 - 8419783

ER -

A Maximum Correntropy Divided Difference Filter for Cooperative Localization

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Keywords

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