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 | |
刊名 | APPLIED SCIENCES-BASEL |
2020 | |
卷号 | 10期号:10页码:1-22 |
关键词 | AUV neural network VI EKF |
ISSN号 | 2076-3417 |
产权排序 | 1 |
英文摘要 | 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. |
资助项目 | Joint fund for equipment pre-research of the Chinese academy of sciences[6141A01060101] |
WOS关键词 | NEURAL-NETWORK ; KALMAN FILTER ; INFERENCE ; BEARING |
WOS研究方向 | Chemistry ; Engineering ; Materials Science ; Physics |
语种 | 英语 |
WOS记录号 | WOS:000541440000074 |
资助机构 | Joint fund for equipment pre-research of the Chinese academy of sciences [6141A01060101] |
内容类型 | 期刊论文 |
源URL | [http://ir.sia.cn/handle/173321/27323] |
专题 | 沈阳自动化研究所_海洋信息技术装备中心 |
通讯作者 | Dong LY(董凌艳) |
作者单位 | 1.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 |
推荐引用方式 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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