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INS/Log Integrated Navigation System with Ocean Current Velocity Model based on Multiple Model Adaptive Estimation

机译:基于多模型自适应估计的洋流速度模型INS / Log组合导航系统

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The performance of a strapdown inertial navigation system aided by a relative speed log is usually affected by ocean current. To tackle this problem, a Multiple Model Adaptive Estimation algorithm to estimate the ocean current velocity is proposed in this paper. Owing to uncertainty of the ocean current velocity, the single model usually fails in representation of its velocity, so we proposes Multiple Model Adaptive Estimation for estimating ocean current velocity, where multiple Kalman filters run in parallel, and state estimation from each filter is combined by computing weight factors and summed with the weighted outputs. By this way we can get the precise ocean current velocity and overcome the effect caused by the velocity relative to water on the filtering performance. The simulation demonstrates that the proposed algorithm can accurately estimate and compensate the ocean current velocity compared with a common Kalman filter algorithm.
机译:相对速度日志辅助的捷联惯性导航系统的性能通常受洋流的影响。为了解决这个问题,本文提出了一种多模型自适应估计算法来估计洋流速度。由于洋流速度的不确定性,单个模型通常无法表示其速度,因此我们提出了用于估计洋流速度的多模型自适应估计,其中多个卡尔曼滤波器并行运行,并且每个滤波器的状态估计通过计算权重因子并与加权输出相加。通过这种方式,我们可以获得精确的洋流速度,并克服了相对于水的速度对过滤性能的影响。仿真表明,与常用的卡尔曼滤波算法相比,该算法可以准确估计和补偿洋流速度。

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