Performance of Neural Networks in Source Localization using Artificial Lateral Line Sensor Configurations

Artificial lateral lines (ALL) are used to detect the movement and locations of sources underwater, and are based on the lateral line organ found in fish and amphibians. Experiments have been performed to evaluate if the localization performance of neural networks, trained on simulated ALL sensor data, can be improved through adjustments of the internal ALL sensor positions. A Cramér-Rao lower bound analysis was performed on a subset of handpicked sensor configurations to estimate the likely performance of various configurations. The best and worst configurations were used to generate simulated datasets with which extreme learning machines (ELMs) and convolutional neural networks (CNNs) were trained and tested on their location accuracy. Simulated datasets consisted of two sources in a three-dimensional basin and the sensor readings of 16 ALL sensors. Results show that the best performing configuration consists of improved ELM and CNN localization performances, while also demonstrating that ELMs are capable of localizing multiple sources in three-dimensional aquatic environments, with comparable if not better results than CNNs.