Source depth classification of passive sonar signals using amplitude statistics.

2010 ◽  
Vol 127 (3) ◽  
pp. 2043-2043
Author(s):  
R. Lee Culver ◽  
Colin W. Jemmott ◽  
Brett E. Bissinger ◽  
N. K. Bose
Keyword(s):  
Source Depth ◽  
Passive Sonar ◽  
Depth Classification

A study on depth classification of defects by machine learning based on hyper-parameter search

Measurement ◽  
2021 ◽  
pp. 110660
Author(s):  
Haoze Chen ◽  
Zhijie Zhang ◽  
Wuliang Yin ◽  
Chenyang Zhao ◽  
Fengxiang Wang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Parameter Search ◽  
Depth Classification

Auditory‐motivated techniques for detection and classification of passive sonar signals

2008 ◽  
Vol 123 (5) ◽  
pp. 3344-3344
Author(s):  
Guy J. Brown ◽  
Robert W. Mill ◽  
Simon Tucker
Keyword(s):  
Passive Sonar

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.

Research on source depth classification using multiple vector hydrophones

2014 ◽  
Author(s):  
Anbang Zhao ◽  
Xuejing Song ◽  
Juan Hui ◽  
Bin Zhou ◽  
Yang Chen
Keyword(s):  
Source Depth ◽  
Depth Classification
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