Reliable underwater dipole source characterization in 3D space by an optimally designed artificial lateral line system. (19th April 2017)

• Record Type: Journal Article
• Authors: Ahrari, Ali; Lei, Hong; Sharif, Montassar Aidi; Deb, Kalyanmoy; Tan, Xiaobo
• Is Part Of: Bioinspiration & biomimetics. Volume 12:Number 3(2017:Sep.)
• Publication Date: 2017-04-19
• Subjects: inverse problem -- bi-level optimization -- uncertain environment -- dipole source characterization -- optimal design; Biomimetics -- Periodicals; Biomedical materials -- Periodicals; Medical innovations -- Periodicals; Biomedical engineering -- Periodicals; 600
• DOI: 10.1088/1748-3190/aa69a4
• Languages: English
• ISSNs: 1748-3182

Abstract

Abstract: Inspired by the lateral line of aquatic vertebrates, an artificial lateral line (ALL) system can localize and track an underwater moving object by analyzing the ambient flow caused by its motion. There are several studies on object detection, localization and tracking by ALL systems, but only a few have investigated the optimal design of the ALL system, the one that on average provides the highest characterization accuracy. Design optimization is particularly important because the uncertainties in the employed flow model and in sensor measurements deteriorate the reliability of sensing. This study investigates the optimal design of the ALL system in three-dimensional (3D) space for dipole source characterization. It highlights some challenges specific to the 3D setting and demonstrates the shortcomings of the designs in which all sensors and their sensing directions are in the same plane. As an alternative, it proposes two design concepts, called 'Offset Strategy' and 'Angle Strategy' to overcome these shortcomings. It investigates potentials of having a swarm of cooperative ALLs as well. It performs design optimization in the presence of sensor and model uncertainties and analyzes the trade-off between the number of sensors and characterization accuracy. The obtained solutions are analyzed to reveal their strategies in solving the problem efficiently. The dependency of the optimized solutions on the uncertainties is also demonstrated.