引用本文: 康小东, 李一平. 基于迭代优化算法的AUV水下运动目标航行参数估计[J]. 机器人, 2022, 44(2): 203-211.
KANG Xiaodong, LI Yiping. Motion Parameters Estimation of Underwater Moving Target Based onIterative Optimization Algorithm for an AUV[J]. ROBOT, 2022, 44(2): 203-211.
 Citation: KANG Xiaodong, LI Yiping. Motion Parameters Estimation of Underwater Moving Target Based onIterative Optimization Algorithm for an AUV[J]. ROBOT, 2022, 44(2): 203-211.

## Motion Parameters Estimation of Underwater Moving Target Based onIterative Optimization Algorithm for an AUV

• 摘要: 为了解决自主水下机器人（autonomous underwater vehicle，AUV）对水下运动目标进行实时动态追踪的技术难题，本文将渐消记忆递推最小二乘算法与平方根算法相结合，提出一种迭代优化算法。该算法充分利用渐消记忆递推最小二乘算法的快速收敛性能，利用平方根算法解决迭代过程中的数值不稳定问题。迭代优化算法能够快速解算出运动目标的初始距离、航向角及运动方向，数值收敛时间约为3 min，目标运动速度信息也能够在5 min左右收敛。该算法的收敛时间短、计算速度快，甚至AUV无需进行任何形式的机动即可令其保持悬停，这些优点使本算法适用于AUV水下运动目标追踪的工程实际问题。

Abstract: In order to solve the technical problem of real-time dynamic tracking of underwater moving targets for autonomous underwater vehicle (AUV), an iterative optimization algorithm is proposed, which combines the fading memory recursive least square (FMRLS) algorithm with the square root algorithm.It makes full use of the fast convergence performance of FMRLS algorithm, and uses the square root algorithm to solve the numerical instability problem in the iterative process.The iterative optimization algorithm can quickly calculate the initial distance, the heading angle and the moving direction of the moving target, and the numerical convergence time is about 3 min, as well as the target moving speed can be converged in about 5 min.With the proposed algorithm, the converge time is short, the computing velocity is high, and moreover, an AUV can keep hovering without any form of maneuverer from itself.Those advantages of the proposed algorithm make it a perfect solution for practical engineering problems of underwater moving target tracking.

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