Use of Signal and Ambient Noise Coherence to Optimize Sonar System Performance

2002 ◽  
Author(s):  
Philip A. Abbot
Keyword(s):  
System Performance ◽  
Ambient Noise ◽  
Sonar System

scholarly journals A spatial correlation model for the horizontal non-isotropic ocean ambient noise vector field

2017 ◽  
Vol 36 (2) ◽  
pp. 124-137 ◽  
Author(s):  
Jianbo Zhou ◽  
Shengchun Piao ◽  
Yiwang Huang ◽  
Shizhao Zhang ◽  
Ke Qu
Keyword(s):  
Vector Field ◽  
Spatial Correlation ◽  
Ambient Noise ◽  
Vibration Velocity ◽  
Ocean Bottom ◽  
Noise Field ◽  
Sonar System ◽  
Sensors Array ◽  
Ocean Environment ◽  
Von Mises

The ocean ambient noise is one of interference fields of underwater acoustic channel. The design and use of any sonar system are bound to be affected by ocean ambient noise, so to research the spatial correlation characteristics of noise field is of positive significance to improving the performance of sonar system. Only wind-generated noise is considered in most existing ambient noise models. In this case, the noise field is isotropic in horizontal direction. However, due to those influencing factors, like rainfall, ships and windstorm, etc. for a real ocean environment, noise field becomes anisotropic horizontally and the spatial structure of ambient field also changes correspondingly. This paper presents a spatial correlation of the acoustic vector field of anisotropic field by introducing Von Mises probability distribution to describe horizontal directivity. Closed-form expressions are derived which relate the cross-correlation among the sound pressure and three orthogonal components of vibration velocity, besides, the influence of the non-uniformity of noise field on the correlation characteristics of noise vector field was analysed. The model presented in this paper can provide theoretical guidance for the design and application of vector sensors array. Furthermore, the achievement could be applied to front extraction, Green’s function extraction, inversion for ocean bottom parameters, and so on.

scholarly journals Underwater Moving Target Classification Using Multilayer Processing of Active Sonar System

2019 ◽  
Vol 9 (21) ◽  
pp. 4617
Author(s):  
Iksu Seo ◽  
Seongweon Kim ◽  
Youngwoo Ryu ◽  
Jungyong Park ◽  
Dong Seog Han
Keyword(s):  
Ambient Noise ◽  
Roc Curves ◽  
Detection Algorithm ◽  
Classification Performance ◽  
Probability Of Detection ◽  
Support Vector ◽  
High Signal ◽  
Classification Rate ◽  
Active Sonar ◽  
Sonar System

The task of detecting and classifying highly maneuverable and unidentified underwater targets in complex environments is significant in active sonar systems. Previous studies have applied many detection schemes to this task using signals above a preset threshold to separate targets from clutter; this is because a high signal-to-noise ratio (SNR) target has sufficient feature vector components to be separated out. However, in real environments, the received target return’s SNR is not always above the threshold. Therefore, a target detection algorithm is needed for varied target SNR conditions. When the clutter energy is too strong, false detection can occur, and the probability of detection is reduced due to the weak target signature. Furthermore, since a long pulse repetition interval is used for long-range detection and ambient noise tends to be high, classification processing for each ping is needed. This paper proposes a multilayer classification algorithm applicable to all signals in real underwater environments above the noise level without thresholding and verifies the algorithm’s classification performance. We obtained a variety of experimental data by using a real underwater target and a hull-mounted active sonar system operated on Korean naval ships in the East Sea, Korea. The detection performance of the proposed algorithm was evaluated in terms of the classification rate and false alarm rate as a function of the SNR. Since experimental environment data, including the sea state, target maneuvering patterns, and sound speed, were available, we selected 1123 instances of ping data from the target over all experiments and randomly selected 1000 clutters based on the distribution of clutters for each ping. A support vector machine was employed as the classifier, and 80% of the data were selected for training, leaving the remaining data for testing. This process was carried out 1000 times. For the performance analysis and discussions, samples of scatter diagrams and feature characteristics are shown and classification tables and receiver operation characteristic (ROC) curves are presented. The results show that the proposed algorithm is effective under a variety of target strengths and ambient noise levels.

Sonar system performance method

2003 ◽  
Vol 113 (2) ◽  
pp. 679
Author(s):  
Michael A. Rosario
Keyword(s):  
System Performance ◽  
Sonar System

Impacts of internal tides on acoustic propagation at sonar frequencies and their effects on sonar system performance

2002 ◽  
Vol 111 (5) ◽  
pp. 2459
Author(s):  
Subramaniam D. Rajan ◽  
James K. Lewis ◽  
Jason Rudzinsky ◽  
Peter J. Stein
Keyword(s):  
System Performance ◽  
Acoustic Propagation ◽  
Internal Tides ◽  
Sonar System
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