Underwater Electro-Location Method Based on Improved Matrix Adaptation Evolution Strategy | Zendy

Yidong Xu | Zendy; Lili Guo | Zendy; Wenjing Shang | Zendy; Yingsong Li | Zendy

AI Assistant Blog Pricing

Home ZAIA Blog

Open Access

Underwater Electro-Location Method Based on Improved Matrix Adaptation Evolution Strategy

Author(s) -

Yidong Xu,

Lili Guo,

Wenjing Shang,

Yingsong Li

Publication year - 2018

Publication title -

ieee access

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.587

H-Index - 127

ISSN - 2169-3536

DOI - 10.1109/access.2018.2855965

Subject(s) - aerospace , bioengineering , communication, networking and broadcast technologies , components, circuits, devices and systems , computing and processing , engineered materials, dielectrics and plasmas , engineering profession , fields, waves and electromagnetics , general topics for engineers , geoscience , nuclear engineering , photonics and electrooptics , power, energy and industry applications , robotics and control systems , signal processing and analysis , transportation

In this paper, a new locating method based on the optimization method for estimating the position of an electric dipole source in underwater environments with a plate uniform circular antenna (PUCA) is presented. The image principle is introduced to build the manifold of the PUCA, greatly reducing the complexity of the manifold. The evaluation function is obtained using the mixed polarization multiple signal classification algorithm, where the minimum value is found using the fast optimal method improved matrix adaptation evolution strategy (MA-ES). In this locating method, the voltage from each channel of the PUCA is the input spatial-temporal data. As a result, the 3-D field components are reduced, and the method can easily be implemented in practical engineering applications. The theoretical analysis and the experiments conducted for both the simulation and the actual received data demonstrate that the accuracy performance of the locating method based on the improved MA-ES is higher than that of the MA-ES and of the covariance MA-ES.

The content you want is available to Zendy users.

Already have an account? Click here to sign in.

Having issues? You can contact us here

Accelerating Research