Underwater dynamic target tracking technology has a wide application prospect in marine resource exploration, underwater engineering operations, naval battlefield monitoring, and underwater precision guidance. Aiming at the underwater dynamic target tracking problem, an autonomous underwater vehicle tracking control method based on trajectory prediction is studied. First, a deep learning-based target detection algorithm is developed. For the image collected by the multibeam forward-looking sonar image, this algorithm uses the YOLO v3 network to determine the target in a sonar image and obtain the position of the target. Then, a time profit Elman neural network (TPENN) is constructed to predict the trajectory information of the dynamic target. Compared with an ordinary Elman neural network, its accuracy of dynamic target prediction is increased. Finally, underwater tracking of the dynamic target is realized using the model predictive controller (MPC), and the tracking result is stable and reliable. Through simulations and experiment, the proposed underwater dynamic target tracking control method is demonstrated to be effective and feasible.

中文翻译：

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基于轨迹预测的自主水下航行器动态目标跟踪控制

水下动态目标跟踪技术在海洋资源勘探、水下工程作业、海军战场监测、水下精确制导等方面具有广泛的应用前景。针对水下动态目标跟踪问题，研究了一种基于轨迹预测的水下航行器自主跟踪控制方法。首先，开发了一种基于深度学习的目标检测算法。该算法针对多波束前视声纳图像采集的图像，利用YOLO v3网络对声纳图像中的目标进行判断，得到目标的位置。然后，构建时间利润Elman神经网络（TPENN）来预测动态目标的轨迹信息。与普通的Elman神经网络相比，提高了动态目标预测的准确性。最后，利用模型预测控制器（MPC）实现了动态目标的水下跟踪，跟踪结果稳定可靠。通过仿真和实验验证了所提水下动态目标跟踪控制方法的有效性和可行性。