Training submerged source detection for a 2D fluid flow sensor array with Extreme Learning Machines

An array of fluid flow sensors can be used to detect and track underwater objects via the fluid flow field these objects create. The sensed flows combine to a spatio-temporal velocity profile, which can be used to solve the inverse problem; determining the relative position and orientation of a moving source via a trained model. In this study, two training strategies are used: simulated data resulting from continuous motion in a path and from vibratory motion at discrete locations on a grid. Furthermore, we investigate two sensing modalities found in literature: 1D and 2D sensitive flow sensors; all while varying the sensor detection threshold via a noise level. Results show that arrays with 2D sensors outperform those with 1D sensors, especially near and next to the sensor array. On average, the path method outperforms the grid method with respect to estimating the location and orientation of a source.