Abst'/> Shallow-sea application of an intelligent fusion module for low-cost sensors in AUV
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Shallow-sea application of an intelligent fusion module for low-cost sensors in AUV

机译：智能融合模块在AUV中低成本传感器的浅海应用

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摘要

AbstractThis paper focuses on the application of AUV in shallow-sea, which environment is more complicated than deep-sea. Owing to independence of external signals, inertial navigation system (INS) has become the most suitable navigation and positioning system for underwater vehicles. However, as the excessive reliance on sensor data, the precision of INS can be affected by external environment, especially heading angles from low-cost sensors such as attitude and heading reference system (AHRS) and digital compass are susceptible to waves and magnetic interference. Therefore, how to use data from low-cost sensors becomes the key to improving navigation performance. Optimally pruned extreme learning machine (OP-ELM) was presented as a more robust and general methodology in 2010, which make it possible to fuse data by using a more reliable method. In this paper, we propose an intelligent fusion module which is designed to obtain the full-noise model for AUV. By judging the state of AHRS and TCM heading angles, intelligent fusion module combines full-noise model with credible data by using OP-ELM to improve the accuracy of positioning and navigation. Our method has been demonstrated by a range of real data, which RMSE can at most improve by 86.4% in complex conditions than Extended Kalman Filter's.Highlights•This paper proposes an intelligent fusion module.•This method is proposed for low-cost sensors in AUV.•The module is designed to obtain the full-noise model of low-cost sensors.•The RMSE of proposed method can be at most improved by 86.4% than Extended Kalman Filter's.•It can easily be transplanted and extended to other sensors.
机译： 摘要 本文重点介绍AUV在浅海环境中的应用，该环境比深海环境要复杂。由于外部信号的独立性，惯性导航系统（INS）已成为最适合水下航行器的导航和定位系统。但是，由于过度依赖传感器数据，INS的精度可能会受到外部环境的影响，尤其是来自低成本传感器（例如姿态和航向参考系统（AHRS）和数字罗盘）的航向角容易受到波和电磁干扰的影响。因此，如何使用低成本传感器的数据成为改善导航性能的关键。最佳修剪的极限学习机（OP-ELM）在2010年作为更健壮和通用的方法被提出，它使使用更可靠的方法融合数据成为可能。在本文中，我们提出了一种智能融合模块，该模块旨在获得AUV的全噪声模型。通过判断AHRS和TCM航向角的状态，智能融合模块通过使用OP-ELM将全噪声模型与可靠数据相结合，以提高定位和导航的准确性。我们的方法已经得到了一系列真实数据的证明，在复杂条件下，RMSE最多可以比扩展卡尔曼滤波的方法提高86.4％。 突出显示 • 本文提出了一种智能融合模块。 • 此方法被建议用于AUV中的低成本传感器。 • 该模块旨在获得低成本传感器的全噪声模型 • 与扩展卡尔曼滤波器相比，所提出方法的RMSE最多可提高86.4％。 < ce：label>• 可以轻松移植并扩展到其他传感器。

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  • 来源
    《Ocean Engineering》 |2018年第15期|386-400|共15页
  • 作者

    Jia Guo; Bo He; Qixin Sha;

  • 作者单位

    School of Information Science and Engineering, Ocean University of China;

    School of Information Science and Engineering, Ocean University of China;

    School of Information Science and Engineering, Ocean University of China;

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  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    AUV; Data fusion; OP-ELM; Neural network;

    机译：AUV;数据融合;OP-ELM;神经网络;

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