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Cooperative Localization and Unknown Currents Estimation Using Multiple Autonomous Underwater Vehicles

机译:使用多个自主水下车辆的合作定位和未知电流估计

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This letter is on cooperative localization and sea currents estimation using multiple Autonomous Underwater Vehicles (AUVs). Due to sea currents, dead reckoning localization based on inertial navigation sensors results in accumulation of localization error. To reduce the error accumulation, this letter proposes navigation algorithm to allow multiple AUVs to simultaneously estimate their navigation states and unknown sea currents. This letter introduces the fusion of Unscented Kalman filter and linear Kalman filter for joint estimation of the navigation states and unknown sea currents. Suppose that only one AUV, called the leader, is equipped with Doppler Velocity Logs (DVL) and Ultra Short Base Line (USBL) sensors. To improve the localization of multiple AUVs, the leader measures both the bearing and the range of its nearby AUV periodically. As far as we know, this manuscript is unique in considering cooperative localization of multiple AUVs in unknown currents. The effectiveness of this cooperative localization is verified using MATLAB simulations.
机译:这封信是使用多个自主水下车辆(AUV)的合作本地化和海流估计。由于海流,基于惯性导航传感器的死估计定位导致累积定位误差。为了减少错误累积,这封信提出了导航算法,以允许多个AUV同时估计其导航状态和未知的海流。这封信介绍了Uncented Kalman滤波器和线性卡尔曼滤波器的融合,用于联合估计导航状态和未知的海流。假设只有一个称为领导者的AUV,配备了多普勒速度日志(DVL)和超短基线(USBL)传感器。为了改善多个AUV的定位,领导者定期测量其附近AUV的轴承和范围。据我们所知,此稿件在考虑在未知电流中的多个AUV的合作定位时是独一无二的。使用MATLAB模拟验证了该合作定位的有效性。

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