RANDI II—An ambient noise model that includes coherent hydrophone summation for sonar system performance and signal processing in shallow and deep water

1983 ◽  
Vol 73 (S1) ◽  
pp. S55-S55
Author(s):  
Ronald A. Wagstaff ◽  
Rachael M. Hamson
Keyword(s):  
Signal Processing ◽  
Deep Water ◽  
System Performance ◽  
Ambient Noise ◽  
Noise Model ◽  
Sonar System

Shipping Noise Predictions: Capabilities and Limitations

2003 ◽  
Vol 37 (4) ◽  
pp. 54-65 ◽  
Author(s):  
Richard M. Heitmeyer ◽  
Stephen C. Wales ◽  
Lisa A. Pflug
Keyword(s):  
Deep Water ◽  
Ambient Noise ◽  
Classical Model ◽  
Propagation Model ◽  
Environmental Data ◽  
Noise Model ◽  
Propagation Models ◽  
Littoral Region ◽  
Model Predictions ◽  
Noise Models

This paper addresses shortcomings in the ability to predict either current levels of the ambient noise generated by shipping or future trends in those levels that might result from changes in the world's shipping fleet. In particular, experimental evidence is presented that predictions of increases in the sound generated by the world's ships based on increases in their speeds and lengths are not justified. This is because, contrary to the classical model of shipping source levels (Ross, 1976), there is a negligible correlation between the source levels of an ensemble of ships and the speeds and lengths of those ships. We also present two examples of noise model predictions that result in large errors. The first shows that two state-of-the-art noise models can yield significantly different noise predictions (5 dB) for the same deep-water, open ocean site. About two dB of this difference is attributed to an approximation inherent in the acoustic propagation model of one of the noise models that is acceptable in some deep-water regions, but not in others. The remaining discrepancy is attributed to differences in the acoustic environmental databases (sound speeds, bathymetries, and geo-acoustic models) between the two noise models. The second example shows that neglecting the local shipping component in a littoral region near a port can result in a noise prediction that is over 15 dB less than a measured value. Taken together, these examples indicate that large errors can result because of inappropriate propagation models or incomplete or inaccurate shipping and environmental data bases. [Work supported by ONR.]

Deep Water Ambient Noise and Mode Processing

2012 ◽  
Author(s):  
Kathleen E. Wage
Keyword(s):  
Deep Water ◽  
Ambient Noise

Deep Water Ambient Noise and Mode Processing

2014 ◽  
Author(s):  
Kathleen E. Wage
Keyword(s):  
Deep Water ◽  
Ambient Noise

scholarly journals A two‐component Arctic ambient noise model

1992 ◽  
Vol 92 (4) ◽  
pp. 2343-2343
Author(s):  
Michael V. Greening ◽  
Pierre Zakarauskas
Keyword(s):  
Ambient Noise ◽  
Noise Model ◽  
Two Component

Spectrum Analysis of Deep Water Ambient Noise in the West Pacific

2021 ◽  
Author(s):  
Jiahua Zhu ◽  
Wei Guo ◽  
Bingbing Zhang ◽  
Yanxin Ma ◽  
Yangyang Chen ◽  
...  
Keyword(s):  
Deep Water ◽  
Spectrum Analysis ◽  
Ambient Noise ◽  
The West ◽  
West Pacific

Intelligent Tracking and Positioning of Targets Using Passive Sensing Systems

2019 ◽  
pp. 286-305
Author(s):  
Saad Iqbal ◽  
Usman Iqbal ◽  
Syed Ali Hassan
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
System Performance ◽  
Target Localization ◽  
The Other ◽  
Conventional System ◽  
Sensing Systems ◽  
Passive Sensing ◽  
Indoor Scenarios ◽  
Localization And Tracking

Target localization and tracking has always been a hot topic in all eras of communication studies. Conventional system used radars for the purpose of locating and/or tracking an object using the classical methods of signal processing. Radars are generally classified as active and passive, where the former uses both transmitter and receivers simultaneously to perform the localization task. On the other hand, passive radars use existing illuminators of opportunity such as wi-fi or GSM signals to perform the aforementioned tasks. Although they perform detection using classical correlation methods and CFAR, recently machine learning has been used in various application of passive sensing to elevate the system performance. The latest developed models for intelligent RF passive sensing system for both outdoor and indoor scenarios are discussed in this chapter, which will give insight to the readers about their designing.

scholarly journals Optimal signal processing algorithm at the background of non-Gaussian flicker noise

2019 ◽  
Vol 30 ◽  
pp. 04016 ◽  
Author(s):  
Alexander Parshin ◽  
Yury Parshin
Keyword(s):  
Signal Processing ◽  
Information Transfer ◽  
Thermal Noise ◽  
Flicker Noise ◽  
Noise Model ◽  
Processing Algorithm ◽  
Signal Processing Algorithm ◽  
Ultra Low Power ◽  
Non Gaussian ◽  
Optimal Signal

The problem of receiving and processing ultra-low-power signals of information transmission systems is being solved. High requirements for energy efficiency on the one hand and a low information transfer rate allows the use of signals with a small spectrum width, including flicker noise spectral regions. A non-Gaussian flicker noise model is used based on a stochastic differential equation with a nonlinear drift coefficient. An optimal signal processing algorithm is being developed against the background of the sum of flicker noise and thermal noise based on an estimated-correlation-compensation approach. The analysis of the effectiveness of optimal signal processing against a background of non-Gaussian flicker noise and thermal noise is carried out.

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