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An Adaptive Target Tracking Algorithm Based on EKF for AUV with Unknown Non-Gaussian Process Noise
Dong LY(董凌艳)1,2,3; Xu HL(徐红丽)1,2; Feng XS(封锡盛)1,2; Han XJ(韩晓军)1,2; Yu C(于闯)1,2
Department海洋信息技术装备中心
Source PublicationAPPLIED SCIENCES-BASEL
ISSN2076-3417
2020
Volume10Issue:10Pages:1-22
Indexed BySCI
WOS IDWOS:000541440000074
Contribution Rank1
Funding OrganizationJoint fund for equipment pre-research of the Chinese academy of sciences [6141A01060101]
KeywordAUV neural network VI EKF
Abstract

An adaptive target tracking method based on extended Kalman filter (TT-EKF) is proposed to simultaneously estimate the state of an Autonomous Underwater Vehicle (AUV) and an mobile recovery system (MRS) with unknown non-Gaussian process noise in homing process. In the application scenario of this article, the process noise includes the measurement noise of AUV heading and forward speed and the estimation error of MRS heading and forward speed. The accuracy of process noise covariance matrix (PNCM) can affect the state estimation performance of the TT-EKF. The variational Bayesian based algorithm is applied to estimate the process noise statistics. We use a Gaussian mixture distribution to model the non-Gaussian noisy forward speed of AUV and MRS. We use a von-Mises distribution to model the noisy heading of AUV and MRS. The variational Bayesian algorithm is applied to estimate the parameters of these distributions, and then the PNCM can be calculated. The prediction error of TT-EKF is online compensated by using a multilayer neural network, and the neural network is online trained during the target tracking process. Matlab simulation and experimental data analysis results verify the effectiveness of the proposed method.

Language英语
WOS SubjectChemistry, Multidisciplinary ; Engineering, Multidisciplinary ; Materials Science, Multidisciplinary ; Physics, Applied
WOS KeywordNEURAL-NETWORK ; KALMAN FILTER ; INFERENCE ; BEARING
WOS Research AreaChemistry ; Engineering ; Materials Science ; Physics
Funding ProjectJoint fund for equipment pre-research of the Chinese academy of sciences[6141A01060101]
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.sia.cn/handle/173321/27323
Collection海洋信息技术装备中心
Corresponding AuthorDong LY(董凌艳)
Affiliation1.State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
2.Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110016, China
3.University of Chinese Academy of Sciences, Beijing 100049, China
Recommended Citation
GB/T 7714
Dong LY,Xu HL,Feng XS,et al. An Adaptive Target Tracking Algorithm Based on EKF for AUV with Unknown Non-Gaussian Process Noise[J]. APPLIED SCIENCES-BASEL,2020,10(10):1-22.
APA Dong LY,Xu HL,Feng XS,Han XJ,&Yu C.(2020).An Adaptive Target Tracking Algorithm Based on EKF for AUV with Unknown Non-Gaussian Process Noise.APPLIED SCIENCES-BASEL,10(10),1-22.
MLA Dong LY,et al."An Adaptive Target Tracking Algorithm Based on EKF for AUV with Unknown Non-Gaussian Process Noise".APPLIED SCIENCES-BASEL 10.10(2020):1-22.
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