A Cooperative Localization Method for AUVs Based on RBF Neural Network-assisted CKF

被引：0

作者：

Xu B. ^{[1
]}

Li S. ^{[1
]}

Jin K. ^{[1
]}

Wang L. ^{[1
]}

机构：

[1] College of Automation, Harbin Engineering University, Harbin, 150001, Heilongjiang

来源：

Binggong Xuebao/Acta Armamentarii | 2019年 / 40卷 / 10期

关键词：

Autonomous underwater vehicle; Cooperative localiztion; Cubature Kalman filter; Radial basis function;

D O I：

10.3969/j.issn.1000-1093.2019.10.018

中图分类号：

学科分类号：

摘要：

For cooperative localization of autonomous underwater vehicles (AUVs), a multi-AUV cooperative localization method based on radial basis function (RBF) neural network-assisted cubature Kalman filter (CKF) is proposed to solve the problem that the cooperative localization performance is restricted by various factors, such as internal and external factors of cooperative localization system. When a basic reference position is available, the filtering innovation, prediction error and filtering gain, which are extracted from the nonlinear filtering CKF, are used as the inputs of the input layer of RBF neural network, and the filtering error value is used as an output to train the RBF neural network. When the reference signal is interrupted, the trained RBF neural network is used to compensate the estimated value of CKF filter state, and then a new estimated state is obtained. The cooperative localization experiment with the input error of multi-AUV cooperative localization system was simulated based on the lake area test data. The experimental results show that the average positioning error of the proposed method is reduced by 70% compared to average positioning error of RBF without CKF. © 2019, Editorial Board of Acta Armamentarii. All right reserved.

引用

页码：2119 / 2128

页数：9

共 21 条

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