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Unsupervised active sonar contact classification through anomaly detection

EURASIP Journal on Advances in Signal Processing, 2023
Target detection and sonar contact classification with active sonar systems are not trivial especially when operating in coastal and shallow water environments with multipath propagation, high reverberation and clutter. It is even more difficult when the
Pietro Stinco, Alessandra Tesei, Kevin D. LePage   +2 more
doaj   +2 more sources

Active Sonar Target Classification Method Based on Fisher’s Dictionary Learning [PDF]

Applied Sciences, 2021
The marine environment is complex and changeable, and the interference of noise and reverberation seriously affects the classification performance of active sonar equipment.
Tongjing Sun, Jiwei Jin, Tong Liu, Jun Zhang   +3 more
doaj   +2 more sources

Bi-Sphere Anomaly Detection With Learnable Centroid for Active Sonar Classification

IEEE Access, 2022
Machine learning (ML)-based approaches are desirable for discriminating targets from clutter signals to enhance the performance of active sonar systems.
Geunhwan Kim, Youngmin Choo
doaj   +2 more sources

Underwater Moving Target Classification Using Multilayer Processing of Active Sonar System [PDF]

Applied Sciences, 2019
The task of detecting and classifying highly maneuverable and unidentified underwater targets in complex environments is significant in active sonar systems.
Iksu Seo, Seongweon Kim, Youngwoo Ryu, Jungyong Park, Dong Seog Han   +4 more
doaj   +2 more sources

Attention-Based Complementary Learning for Active Target Classification With Limited Sonar Data

IEEE Access
We propose an active sonar target classifier using attention-based complementary learning (ABCL) to mitigate poor generalization with scarce active sonar data.
Youngsang Hwang, Geunhwan Kim, Sungjin Shin, Wooyoung Hong, Youngmin Choo   +4 more
doaj   +2 more sources

A Survey of Underwater Acoustic Data Classification Methods Using Deep Learning for Shoreline Surveillance

Sensors, 2022
This paper presents a comprehensive overview of current deep-learning methods for automatic object classification of underwater sonar data for shoreline surveillance, concentrating mostly on the classification of vessels from passive sonar data and the ...
Lucas C. F. Domingos, Paulo E. Santos, Phillip S. M. Skelton, Russell S. A. Brinkworth, Karl Sammut   +4 more
doaj   +1 more source

Active Sonar Target Classification with Power-Normalized Cepstral Coefficients and Convolutional Neural Network

Applied Sciences, 2020
Detection and classification of unidentified underwater targets maneuvering in complex underwater environments are critical for active sonar systems.
Seungwoo Lee, Iksu Seo, Jongwon Seok, Yunsu Kim, Dong Seog Han   +4 more
doaj   +1 more source

Multi-Layer Perceptron Training Optimization Using Nature Inspired Computing

IEEE Access, 2022
Although the multi-layer perceptron (MLP) neural networks provide a lot of flexibility and have proven useful and reliable in a wide range of classification and regression problems, they still have limitations.
Ali Al Bataineh, Devinder Kaur, Seyed Mohammad J. Jalali   +2 more
doaj   +1 more source

Feature Extraction and Classification of Simulated Monostatic Acoustic Echoes from Spherical Targets of Various Materials Using Convolutional Neural Networks

Journal of Marine Science and Engineering, 2023
Active sonar target classification remains an ongoing area of research due to the unique challenges associated with the problem (unknown target parameters, dynamic oceanic environment, different scattering mechanisms, etc.).
Bernice Kubicek, Ananya Sen Gupta, Ivars Kirsteins   +2 more
doaj   +1 more source

Underwater Acoustic Research Trends with Machine Learning: Active SONAR Applications

한국해양공학회지, 2020
Underwater acoustics, which is the study of phenomena related to sound waves in water, has been applied mainly in research on the use of sound navigation and range (SONAR) systems for communication, target detection, investigation of marine resources and
Haesang Yang, Sung-Hoon Byun, Keunhwa Lee, Youngmin Choo, Kookhyun Kim   +4 more
doaj   +1 more source