The lateral line enables fish to efficiently sense the surrounding environment, thus assisting flow-related fish behaviors. Inspired by this phenomenon, varieties of artificial lateral line systems (ALLSs) have been developed and applied to underwater robots. This article focuses on using the pressure sensor arrays based ALLS-measured hydrodynamic pressure variations (HPVs) for estimating the relative states between an upstream oscillating fin and a downstream robotic fish. The HPVs and relative states are measured in flume experiments in which the oscillating fin and the robotic fish have been locate with upstream-downstream formation in a flume. The relative states include the relative oscillating frequency, amplitude, and offset of the upstream oscillating fin to the downstream robotic fish, the relative vertical distance, the relative yaw angle, the relative pitch angle, and the relative roll angle between the upstream oscillating fin and the downstream robotic fish. Regression models between the ALLS-measured and the mentioned relative states are investigated, and regression models-based relative state estimations are conducted. Specifically, two criteria are proposed firstly to investigate not only the sensitivity of each pressure sensor to the variations of relative state but also the insufficiency and redundancy of the pressure sensors. And thus the pressure sensors used for regression analysis are determined. Then four typical regression methods, including random forest (RF) algorithm, support vector regression, back propagation neural network, and multiple linear regression method are used for establishing regression models between the ALLS-measured HPVs and the relative states. Then regression effects of the four methods are compared and discussed. Finally, the RF-based method, which has the best regression effect, is used to estimate the relative yaw angle and oscillating amplitude using the ALLS-measured HPVs and exhibits excellent estimation performance.

侧线使鱼类能够有效地感知周围环境，从而协助与流动相关的鱼类行为。受这种现象的启发，人们开发了多种人工侧线系统（ALLS）并将其应用于水下机器人。本文重点介绍使用基于 ALLS 测量的流体动力压力变化 (HPV) 的压力传感器阵列来估计上游摆动鳍和下游机器鱼之间的相对状态。HPV 和相关状态是在水槽实验中测量的，在水槽实验中，摆动鳍和机器鱼已经定位在水槽中的上游-下游形成。相对状态包括上游摆动鳍与下游机器鱼的相对摆动频率、振幅和偏移量，相对垂直距离，上游摆动鳍与下游机器鱼之间的相对偏航角、相对俯仰角和相对横摇角。研究了 ALLS 测量和提到的相对状态之间的回归模型，并进行了基于回归模型的相对状态估计。具体来说，首先提出了两个标准，不仅调查每个压力传感器对相对状态变化的敏感性，还调查压力传感器的不足和冗余。从而确定了用于回归分析的压力传感器。然后是四种典型的回归方法，包括随机森林（RF）算法、支持向量回归、反向传播神经网络、采用多元线性回归方法建立AL​​LS测量的HPV与相关状态之间的回归模型。然后对四种方法的回归效果进行了比较和讨论。最后，基于射频的方法具有最佳回归效果，用于使用 ALLS 测量的 HPV 估计相对偏航角和振荡幅度，并表现出优异的估计性能。