Technology of Information Security
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App. Res. of Comp. Printed Article
Vol. 36, 2019 No. 2
570-573

Augmented target state estimation algorithm with systematic errors based on maximum correntropy unscented Kalman filter in sensor networks

Zhao Jihong1,2
Xie Zhiyong1
Qu Hua2
Wang Mingxin1
Liu Xi2
1. School of Communication & Information Engineering, Xi'an University of Posts & Telecommunications, Xi'an 710121, China
2. School of Electronic & Information Engineering, Xi'an Jiaotong University, Xi'an 710049, China
DOI: 10.19734/j.issn.1001-3695.2017.08.0884

Abstract

This paper proposed a method that combined target state and system error based on the maximum correntropy criterion unscented Kalman filter(MCUKF), namely ASMCUKF, which could solve the problems that the observation noise of sensor was heavy-tailed or had some sudden change in space-air-ground integrated sensor network. Firstly, MCUKF used unscented transformation(UT) to obtain the predicted state estimation and covariance matrix, and then used a non-linear regression model based on MCC to reconstruct the observed information and strengthen the robustness of UKF in heavy-tailed noise. The ASMCUKF algorithm established the state equation and the nonlinear observation equation with systematic errors through the expansion of target state vector, and made the error registration according to the estimated system errors to improve the influence of the system errors on the target state estimation. The simulation results show that the ASMCUKF has a better performance for the state estimation of the communication target than the traditional method in the environment of heavy-tailed non- Gaussian noise.

Foundation Support

国家自然科学基金资助项目(61371087,61531013)
国家“863”计划资助项目(2015AA015702)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2017.08.0884
Publish at: Application Research of Computers Printed Article, Vol. 36, 2019 No. 2
Section: Technology of Information Security
Pages: 570-573
Serial Number: 1001-3695(2019)02-055-0570-04

Publish History

[2019-02-05] Printed Article

Cite This Article

赵季红, 谢志勇, 曲桦, 等. 基于最大互相关熵UKF的传感网目标状态和系统偏差联合估计算法 [J]. 计算机应用研究, 2019, 36 (2): 570-573. (Zhao Jihong, Xie Zhiyong, Qu Hua, et al. Augmented target state estimation algorithm with systematic errors based on maximum correntropy unscented Kalman filter in sensor networks [J]. Application Research of Computers, 2019, 36 (2): 570-573. )

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  • Application Research of Computers Monthly Journal
  • Journal ID ISSN 1001-3695
    CN  51-1196/TP

Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

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