Sensor placement optimization in the artificial lateral line using optimal weight analysis combining feature distance and variance evaluation

Abstract

Artificial lateral line is a multi-sensor system, mimicking the lateral line of fish to perceive the parameters of flow field. However, it can easily lead to information loss or redundancy with limited number of sensors due to unsuitable sensor placement. An optimal weight analysis algorithm is proposed to solve the problem on sensor placement of robotic fish. Firstly, signal features are extracted from the pressure data, which are collected from candidate sensor locations in different conditions. Then the improved distance evaluation is used to assess each feature, and the feature distance factor is regarded as the weight for distinguishing. Combined with the analysis of variance, the contribution vector of sensor locations is obtained. Three indexes selected by the algorithm are introduced to compare the sensor subsets. The results in both simulation and experiment show the effectiveness of the algorithm. The optimal number of sensors on the robotic fish is also studied.