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

被引：47

作者：

Deng, Zhihong ^{[1
]}

Shi, Lei ^{[1
]}

Yin, Lijian ^{[2
]}

Xia, Yuanqing ^{[1
]}

Huo, Baoyu ^{[2
]}

机构：

[1] Beijing Inst Technol, Sch Automat, Beijing 100081, Peoples R China

[2] China Construct Eighth Engn Div, Construct Ltd Co 2, Jinan 250014, Peoples R China

来源：

IEEE SENSORS JOURNAL | 2020年 / 20卷 / 14期

基金：

中国国家自然科学基金;

关键词：

Kernel; Sensors; Bandwidth; Kalman filters; Mathematical model; Time measurement; Robustness; Maximum correntropy criterion; unscentedKalman filter; intermittent observations; non-gaussian noise; RECURSIVE ESTIMATION; STATE ESTIMATORS; UNSCENTED KALMAN; SYSTEMS; FILTERS;

D O I：

10.1109/JSEN.2020.2980354

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：

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.

引用

页码：7766 / 7773

页数：8

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