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Cooperative localization of AUVs using moving horizon estimation

机译:使用移动视界估计的AUV合作定位

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This paper studies the localization problem of autonomous underwater vehicles (AUVs) constrained by limited size, power and payload. Such AUVs cannot be equipped with heavy sensors which makes their underwater localization problem difficult. The proposed cooperative localization algorithm is performed by using a single surface mobile beacon which provides range measurement to bound the localization error. The main contribution of this paper is twofold: 1) The observability of single beacon based localization is first analyzed in the context of nonlinear discrete time system, deriving a sufficient condition on observability. It is further compared with observability of linearized system to verify that a nonlinear state estimation is necessary. 2) Moving horizon estimation is integrated with extended Kalman filter (EKF) for three dimensional localization using single beacon, which can alleviate the computational complexity, impose various constraints and make use of several previous range measurements for each estimation. The observability and improved localization accuracy of the localization algorithm are verified by extensive numerical simulation compared with EKF.
机译:本文研究了受尺寸,功率和有效载荷限制的自动水下航行器(AUV)的定位问题。这种AUV无法配备重型传感器,这使其水下定位问题变得困难。所提出的协同定位算法是通过使用单表面移动信标执行的,该信标提供范围测量以限制定位误差。本文的主要贡献有两个方面:1)首先在非线性离散时间系统的背景下分析了基于单个信标的定位的可观测性,从而为可观测性提供了充分的条件。进一步将其与线性化系统的可观察性进行比较,以验证是否需要进行非线性状态估计。 2)移动视野估计与扩展的卡尔曼滤波器(EKF)集成在一起,可使用单个信标进行三维定位,从而减轻了计算复杂性,施加了各种限制并为每个估计使用了多个先前的距离测量值。与EKF相比,通过广泛的数值模拟验证了定位算法的可观察性和提高的定位精度。

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