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

机译:自动水下车辆的MEMS IMU和单向旅行时间导航

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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:没有全球定位系统(GPS)是对水下环境导航和定位的挑战。因此,为了精确和准确的导航,自主水下车辆(AUV)通常依赖于非常精确的(又非常昂贵且高能量消耗)惯性带状传感器,其需要外部观察以限制其死估计的位置误差随着时间的推移 - 例如,多普勒速度原木(DVL)或声学定位系统。声学导航方法的最新进步是通过提供外部位置校正,从而使AUV扩展淹没的任务时间,从而限制位置误差。此外,微机电系统(MEMS)惯性传感器技术的进步急剧降低了惯性传感器的尺寸,功耗和成本;但是,它们仍然嘈杂并随着时间的推移累积错误。本文在最近的单架单向旅行时间(OWTT)声学导航中建立了最近的进步,并通过最小的导航表置传感器套件调查小型AUV的边界位置误差程度,依赖于消费级MEMS惯性测量单位(IMU)和车辆的动态模型速度。在具有两个Ocean-Server,Inc.Iver2车辆的领域中获得了包含偏置估计的扩展卡尔曼滤波器(EKF)的实时实现,并且结果显示了10.26米和12.95米的平均位置误差分别为1.73公里和1.91公里。

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