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MEMS IMU and one-way-travel-time navigation for autonomous underwater vehicles

机译:自主水下航行器的MEMS IMU和单程旅行导航

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The absence of the global positioning system (GPS) is a challenge to navigation and localization in the underwater environment. Thus, for precise and accurate navigation, autonomous underwater vehicles (AUVs) often rely on very precise (yet very expensive and high energy consumption) inertial strap-down sensors which require external observations to constrain their dead-reckoned position error over time - e.g., Doppler velocity logs (DVLs) or acoustic positioning systems. Recent advances in acoustic navigation methodologies are enabling AUVs to extend submerged mission time by providing external position corrections and thereby constraining position error. Additionally, advances in microelectromechanical system (MEMS) inertial sensor technology has drastically lowered the size, power consumption, and cost of inertial sensors; however, they are still noisy and accumulate error over time. This paper builds on recent advances in single beacon one-way-travel-time (OWTT) acoustic navigation and investigates the degree of bounding position error for small AUVs with a minimal navigation strap-down sensor suite, relying on a consumer grade MEMS inertial measurement unit (IMU) and a vehicle's dynamic model velocity. A real time implementation of an Extended Kalman Filter (EKF), containing bias estimation, is obtained in the field with two Ocean-Server, Inc. Iver2 vehicles, and results show an average position error of 10.26 meters and 12.95 meters over a distance traveled of 1.73 kilometers and 1.91 kilometers, respectively.
机译:缺少全球定位系统(GPS)是对水下环境中导航和定位的挑战。因此,为了进行精确,精确的导航,自动驾驶水下航行器(AUV)通常依赖于非常精确的(但价格昂贵且能耗高)惯性捷联传感器,该传感器需要外部观察以限制其随时间变化的死角位置误差,例如,多普勒速度测井(DVL)或声学定位系统。声学导航方法的最新进展使AUV可以通过提供外部位置校正来延长水下任务时间,从而限制位置误差。另外,微机电系统(MEMS)惯性传感器技术的进步大大降低了惯性传感器的尺寸,功耗和成本。但是,它们仍然很嘈杂,并且随着时间的推移会累积错误。本文基于单信标单程行进(OWTT)声导航的最新进展,并基于消费级MEMS惯性测量,以最小的导航捷联传感器套件研究了小型AUV的边界位置误差程度。单位(IMU)和车辆的动力学模型速度。使用两台Ocean-Server,Inc. Iver2车辆在现场获得了包含偏差估计的扩展卡尔曼滤波器(EKF)的实时实现,结果显示在行驶的整个距离上平均位置误差为10.26米和12.95米分别为1.73公里和1.91公里

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