scholarly journals Deep‐learning‐based line enhancer for passive sonar systems

2021 ◽  
Author(s):  
Donghao Ju ◽  
Cheng Chi ◽  
Zigao Li ◽  
Yu Li ◽  
Chunhua Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Passive Sonar ◽  
Sonar Systems

Sonar operator performance considerations in passive sonar systems

1975 ◽  
Author(s):  
R. J. Hornick ◽  
G. Yamashita ◽  
J. E. Robinson ◽  
H. J. Winkler
Keyword(s):  
Operator Performance ◽  
Passive Sonar ◽  
Sonar Systems ◽  
Performance Considerations

scholarly journals A Review on Deep Learning-Based Approaches for Automatic Sonar Target Recognition

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1972
Author(s):  
Dhiraj Neupane ◽  
Jongwon Seok
Keyword(s):  
Deep Learning ◽  
Target Recognition ◽  
Rapid Development ◽  
Automatic Target Recognition ◽  
Data Driven ◽  
Underwater Sound ◽  
Sonar Systems ◽  
Sonar Signal Processing ◽  
Types Of Information ◽  
Data Acquisition Process

Underwater acoustics has been implemented mostly in the field of sound navigation and ranging (SONAR) procedures for submarine communication, the examination of maritime assets and environment surveying, target and object recognition, and measurement and study of acoustic sources in the underwater atmosphere. With the rapid development in science and technology, the advancement in sonar systems has increased, resulting in a decrement in underwater casualties. The sonar signal processing and automatic target recognition using sonar signals or imagery is itself a challenging process. Meanwhile, highly advanced data-driven machine-learning and deep learning-based methods are being implemented for acquiring several types of information from underwater sound data. This paper reviews the recent sonar automatic target recognition, tracking, or detection works using deep learning algorithms. A thorough study of the available works is done, and the operating procedure, results, and other necessary details regarding the data acquisition process, the dataset used, and the information regarding hyper-parameters is presented in this article. This paper will be of great assistance for upcoming scholars to start their work on sonar automatic target recognition.

Receiver depth selection for passive sonar systems

1991 ◽  
Vol 16 (3) ◽  
pp. 267-278 ◽  
Author(s):  
C. Ferla ◽  
M.B. Porter
Keyword(s):  
Passive Sonar ◽  
Sonar Systems ◽  
Selection For

SYNTHESIS OF NEURAL NETWORK ALGORITHMS FOR CLASSIFICATION OF MARINE OBJECTS IN LOW-FREQUENCY PASSIVE SONAR SYSTEMS

2019 ◽  
pp. 3-8
Author(s):  
O.A. Andreev ◽  
A.T. Trofimov
Keyword(s):  
Neural Network ◽  
Probabilistic Models ◽  
Low Frequency ◽  
Gaussian Mixture ◽  
Classification Systems ◽  
Classification Algorithms ◽  
Correct Classification ◽  
Passive Sonar ◽  
Sonar Systems

The paper addresses the issue of insuring the required probability of correct classification of marine objects in low-frequency passive sonar systems. The solution to the issue is sought through the application of methods for the synthesis of neural network classification algorithms using poly-Gaussian probabilistic models (Gaussian mixture models, GMM). It is shown that the use of GMM makes it possible to solve a number of problems specific to the issue; classification algorithms synthesized using mentioned methods can be implemented in the form of neural networks, which in turn can be described in C++/VHDL to create endpoint computing devices or software systems. The results of modeling of synthesized classification algorithms on experimental data are presented; it is demonstrated that such algorithms make it possible to increase the probability of correct classification of marine objects and to satisfy typical requirements for classification systems in low-frequency passive sonar systems.

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