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基于单信标传输时间差的水下滑翔机模型辅助定位与导航

Model-Aided Localization and Navigation for Underwater Gliders Using Single-Beacon Travel-Time Differences.

机构信息

State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China.

Institutes for Robotics and Intelligent Manufacturing, Chinese Academy of Sciences, Shenyang 110169, China.

出版信息

Sensors (Basel). 2020 Feb 7;20(3):893. doi: 10.3390/s20030893.

Abstract

An accurate motion model and reliable measurements are required for autonomous underwater vehicle localization and navigation in underwater environments. However, without a propeller, underwater gliders have limited maneuverability and carrying capacity, which brings difficulties for modeling and measuring. In this paper, an extended Kalman filter (EKF)-based method, combining a modified kinematic model of underwater gliders with the travel-time differences between signals received from a single beacon, is proposed for estimating the glider positions in a predict-update cycle. First, to accurately establish a motion model for underwater gliders moving in the ocean, we introduce two modification parameters, the attack and drift angles, into a kinematic model of underwater gliders, along with depth-averaged current velocities. The attack and drift angles are calculated based on the coefficients of hydrodynamic forces and the sensor-measured angle variation over time. Then, instead of satisfying synchronization requirements, the travel-time differences between signals received from a single beacon, multiplied by the sound speed, are taken as the measurements. To further reduce the EKF estimation error, the Rauch-Tung-Striebel (RTS) smoothing method is merged into the EKF system. The proposed method is tested in a virtual spatiotemporal environment from an ocean model. The experimental results show that the performance of the RTS-EKF estimate is improved when compared with the motion model estimate, especially by 46% at the inflection point, at least in the particular study developed in this article.

摘要

在水下环境中,自主水下航行器需要精确的运动模型和可靠的测量来实现定位和导航。然而,没有推进器的水下滑翔机机动性和承载能力有限,这给建模和测量带来了困难。本文提出了一种基于扩展卡尔曼滤波器(EKF)的方法,将改进的水下滑翔机运动模型与单个信标接收到的信号之间的传播时间差相结合,在预测-更新周期中估计滑翔机的位置。首先,为了准确建立在海洋中运动的水下滑翔机的运动模型,我们引入了两个修正参数,攻角和漂移角,以及深度平均海流速度。攻角和漂移角是根据水动力系数和传感器测量的随时间变化的角度变化计算的。然后,我们不满足同步要求,而是将单个信标接收到的信号之间的传播时间差乘以声速作为测量值。为了进一步降低 EKF 估计误差,将 Rauch-Tung-Striebel(RTS)平滑方法合并到 EKF 系统中。所提出的方法在海洋模型的虚拟时空环境中进行了测试。实验结果表明,与运动模型估计相比,RTS-EKF 估计的性能得到了提高,特别是在拐点处提高了 46%,至少在本文开发的特定研究中是这样。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/29fc/7039302/d8d10c2cecec/sensors-20-00893-g001.jpg

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