UKF Based on Maximum Correntropy Criterion in the Presence of Both Intermittent Observations and Non-Gaussian Noise

Zhihong Deng; Lei Shi; Lijian Yin; Yuanqing Xia; Baoyu Huo

doi:10.1109/JSEN.2020.2980354

UKF Based on Maximum Correntropy Criterion in the Presence of Both Intermittent Observations and Non-Gaussian Noise

Zhihong Deng^*, Lei Shi, Lijian Yin, Yuanqing Xia, Baoyu Huo

^*Corresponding author for this work

School of Automation

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

34 Citations (Scopus)

Abstract

Motivated by tracking applications with sensor networks under non-Gaussian noise and intermittent observations, this paper considers a maximum correntropy unscented Kalman filter (MCUKF). MCUKF is based on maximum correntropy criterion (MCC) and unscented transformation (UT) which can deal with both non-Gaussian noise and intermittent observations. The intermittent observations are described by a binary sequence satisfying some properties. The MCC is used to deal with non-Gaussian noise and improves the robustness. Moreover, the arrival probabilities under non-Gaussian noise (shot noise and Gaussian mixture noise) and intermittent observations are given. The performance of the presented algorithm is verified by illustrating numerical examples.

Original language	English
Article number	9034028
Pages (from-to)	7766-7773
Number of pages	8
Journal	IEEE Sensors Journal
Volume	20
Issue number	14
DOIs	https://doi.org/10.1109/JSEN.2020.2980354
Publication status	Published - 15 Jul 2020

Keywords

Maximum correntropy criterion
intermittent observations
non-Gaussian noise
unscentedKalman filter

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10.1109/JSEN.2020.2980354

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abstract = "Motivated by tracking applications with sensor networks under non-Gaussian noise and intermittent observations, this paper considers a maximum correntropy unscented Kalman filter (MCUKF). MCUKF is based on maximum correntropy criterion (MCC) and unscented transformation (UT) which can deal with both non-Gaussian noise and intermittent observations. The intermittent observations are described by a binary sequence satisfying some properties. The MCC is used to deal with non-Gaussian noise and improves the robustness. Moreover, the arrival probabilities under non-Gaussian noise (shot noise and Gaussian mixture noise) and intermittent observations are given. The performance of the presented algorithm is verified by illustrating numerical examples.",

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AU - Deng, Zhihong

AU - Shi, Lei

AU - Yin, Lijian

AU - Xia, Yuanqing

AU - Huo, Baoyu

PY - 2020/7/15

Y1 - 2020/7/15

N2 - Motivated by tracking applications with sensor networks under non-Gaussian noise and intermittent observations, this paper considers a maximum correntropy unscented Kalman filter (MCUKF). MCUKF is based on maximum correntropy criterion (MCC) and unscented transformation (UT) which can deal with both non-Gaussian noise and intermittent observations. The intermittent observations are described by a binary sequence satisfying some properties. The MCC is used to deal with non-Gaussian noise and improves the robustness. Moreover, the arrival probabilities under non-Gaussian noise (shot noise and Gaussian mixture noise) and intermittent observations are given. The performance of the presented algorithm is verified by illustrating numerical examples.

AB - Motivated by tracking applications with sensor networks under non-Gaussian noise and intermittent observations, this paper considers a maximum correntropy unscented Kalman filter (MCUKF). MCUKF is based on maximum correntropy criterion (MCC) and unscented transformation (UT) which can deal with both non-Gaussian noise and intermittent observations. The intermittent observations are described by a binary sequence satisfying some properties. The MCC is used to deal with non-Gaussian noise and improves the robustness. Moreover, the arrival probabilities under non-Gaussian noise (shot noise and Gaussian mixture noise) and intermittent observations are given. The performance of the presented algorithm is verified by illustrating numerical examples.

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UKF Based on Maximum Correntropy Criterion in the Presence of Both Intermittent Observations and Non-Gaussian Noise

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