NDLI: Neural network for underwater target detection

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[1991 Proceedings] IEEE Conference on Neural Networks for Ocean Engineering

IEEE Conference on Neural Networks for Ocean Engineering (Cat. No.91CH3064-3)

Neural-network performance assessment in sonar applications

All neural network sonar discrimination system

An episodal neural-net computing approach to the detection and interpretation of underwater acoustic transients

Acoustic transient analysis using wavelet decomposition

Adaptive kernel classifiers for short-duration oceanic signals

A neural network-based passive sonar detection and classification design with a low false alarm rate

Minehunting with multi-layer perceptrons

Constrained neural networks for recognition of passive sonar signals using shape

Sonar scene analysis using neurobionic sound segregation

Neural network for underwater target detection

Texture estimation with neural networks

Applications of neural networks to ocean acoustic tomography

Experimental comparison between neural networks and classical techniques of classification applied to natural underwater transients identification

Recent advances in pattern recognition and their potential application in active sonar classification

Modeling constant best delay-sensitive neurons and tracking neurons in the auditory cortex of the FM bat with a back-propagation neural network

A neural network for data association in a multiple-target tracking system

Mission adaptable autonomous vehicles

Design of an intelligent control system for remotely operated vehicles

Neural network control of a robotic manipulator arm for undersea applications

An integrated neurocomputing architecture for side-scan sonar target detection

Continuous inference networks for autonomous systems

Neural network learning rules for control: application to AUV tracking control

Underwater robotic operations using a decentralized adaptive neurocontroller

The potential of a neural network based sonar system in classifying fish

Neural network modeling of radar backscatter from an ocean surface using chaos theory

Classification of euceratium gran. in neural networks

Mapping of ocean sediments, by networks of parallel interpolating units

Model based classification of transient signals using the MLANS neural network

Neural-network-based classification of acoustic transients

ARACHYDE: a sensor-to-situation assessment software architecture for passive acoustic signal understanding

Evaluation of neural network and conventional techniques for sonar signal discrimination

Artificial neural networks for simultaneous and independent range and depth discrimination in passive acoustic localization

Classification of underwater acoustic transients by artificial neural networks

Neural networks and the classification of complex sonar signals

Fuzzy min-max classification with neural networks

An overview of the CMAC neural network

The Lockheed probabilistic neural network processor

Evolutionary methods for training neural networks

A new design methodology for optimal interpolative neural networks with application to the localization and classification of acoustic transients

ARTMAP: supervised real-time learning and classification of nonstationary data by a self-organizing neural network

Calibrating the performance of neural networks

A characterization of neural network performances based on Fokker-Planck statistical models

Neural networks for classification of ARMA models: an experimental study

Fundamental neural structures, operations, and asymptotic performance criteria in decentralized binary hypothesis testing

Neural network for underwater target detection

Content Provider	IEEE Xplore Digital Library
Author	Eapen, A.
Copyright Year	1991
Description	Author affiliation: Naval Phys. & Oceanogr. Lab., Cochin, India (Eapen, A.)
Abstract	The author proposes the use of a neural network for detecting underwater targets in the presence of random noise. The neutral network is trained to analyze fixed time frames of the input signal to detect the presence or absence of the target, during which the network gets adapted to the local environment and learns to identify the features of the targets. A multilayer neural network is trained to correctly classify many example patterns with and without the target signal present. The back propagation learning rule is employed to update the weights on every presentation of input frames. Once the training is complete the network would be able to tell whether the input frame presented to it contains any target signature.<>
Starting Page	91
Ending Page	98
File Size	424611
Page Count	8
File Format	PDF
ISBN	0780302052
DOI	10.1109/ICNN.1991.163331
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1991-08-15
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Acoustic noise Neural networks Neurons Noise reduction Object detection Artificial neural networks Signal processing Pattern recognition Biological neural networks Multi-layer neural network
Content Type	Text
Resource Type	Article

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