Accurate prediction of acoustic communication performance is an important capability for marine robots. In this paper, we propose a model-based learning methodology for the prediction of underwater acoustic communication performance. The learning algorithm consists of two steps: (i) estimation of the covariance matrix by evaluating candidate functions with estimated parameters using detrended measurements;and (ii) prediction of communication performance. Covariance estimation is addressed with a multi-stage iterative training method that produces unbiased and robust results with nested models. The efficiency of the framework is validated with simulations and experimental data from field trials. The field trials involved a manned surface vehicle and an autonomous underwater vehicle.

声音通信性能的准确预测是船用机器人的一项重要功能。在本文中，我们提出了一种基于模型的学习方法，用于预测水下声通信性能。该学习算法包括两个步骤：（i）通过使用去趋势的测量通过使用估计参数评估候选函数来估计协方差矩阵；以及（ii）预测通信性能。使用多阶段迭代训练方法解决协方差估计，该方法使用嵌套模型可产生无偏且鲁棒的结果。框架的效率已通过仿真和来自现场试验的实验数据进行了验证。现场试验涉及有人驾驶水面载具和水下自动驾驶仪。