In this article, we propose a nonlinear model based on hidden-layer neural networks and local Gaussian process regression. The hidden-layer neural networks have short training time, whereas the local Gaussian process regression is more suitable for nonlinearity. According to the abovementioned advantages of hidden-layer neural network and local Gaussian process regression, we get the measurement learning model and global process learning model offline and local process learning model online. Then, the proposed learning-based models have been applied to extended Kalman filter-simultaneous localization and mapping (EKF-SLAM), Rao-Blackwellised particle filter formulation of simultaneous localisation and mapping (FastSLAM), and unscented Kalman filter--simultaneous localization and mapping (UKF-SLAM) for autonomous underwater vehicles position tracking with field experiments. Compared with the conventional process and measurement models, improved position tracking performance can be achieved without cumbersome system analysis nor identification, benefiting from the proposed model. Experiments in more than 3883-m run show that the root-mean-square error has been improved by 36.95%, 37.14%, and 27.65% in EKF-SLAM, FastSLAM, and UKF-SLAM frameworks, respectively.

中文翻译：

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AUV位置跟踪的通用学习建模

在本文中，我们提出了一种基于隐藏层神经网络和局部高斯过程回归的非线性模型。隐层神经网络训练时间短，而局部高斯过程回归更适合非线性。根据上述隐藏层神经网络和局部高斯过程回归的优点，我们得到了离线的测量学习模型和全局过程学习模型以及在线的局部过程学习模型。然后，提出的基于学习的模型已应用于扩展卡尔曼滤波器-同时定位和映射（EKF-SLAM），同时定位和映射的 Rao-Blackwellised 粒子滤波器公式（FastSLAM），和无味卡尔曼滤波器——同时定位和映射 (UKF-SLAM) 用于通过现场实验进行自主水下航行器位置跟踪。与传统的过程和测量模型相比，无需繁琐的系统分析或识别，就可以实现改进的位置跟踪性能，受益于所提出的模型。超过 3883 米运行的实验表明，EKF-SLAM、FastSLAM 和 UKF-SLAM 框架的均方根误差分别提高了 36.95%、37.14% 和 27.65%。