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# IEEE Journal of Oceanic Engineering

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Displaying Results 1 - 10 of 10
• ### On-line learning control of autonomous underwater vehicles using feedforward neural networks

Publication Year: 1992, Page(s):308 - 319
Cited by:  Papers (45)
| | PDF (932 KB)

A neural-network-based learning control scheme for the motion control of autonomous underwater vehicles (AUV) is described. The scheme has a number of advantages over the classical control schemes and conventional adaptive control techniques. The dynamics of the controlled vehicle need not be fully known. The controller with the aid of a gain layer learns the dynamics and adapts fast to give the c... View full abstract»

• ### Using evolutionary programming for modeling: an ocean acoustic example

Publication Year: 1992, Page(s):333 - 340
Cited by:  Papers (7)
| | PDF (684 KB)

The process of natural evolution is used as a basis for a search technique that can locate the extremum of complex response surfaces despite the existence of multiple local minima or maxima. Background on research in simulated evolution is offered. The fundamental properties of natural evolution are simulated for the purpose of modeling a set of ocean acoustic signals. The experimental results ind... View full abstract»

• ### Fish detection and identification using neural networks-some laboratory results

Publication Year: 1992, Page(s):364 - 368
Cited by:  Papers (14)
| | PDF (424 KB)

Tests on laboratory data on the use of neural networks to detect and identify fish from their sonar echoes are reported. Results are quite encouraging; simple three-layer perceptrons trained on a portion of the data set are able to recognize over 80% of the targets on the remainder of the data set. Parallel networks are found to be very effective, and a parallel combination of two networks (featur... View full abstract»

• ### Real-time implementation of propagator' bearing estimation algorithm by use of neural network

Publication Year: 1992, Page(s):320 - 325
Cited by:  Papers (3)
| | PDF (420 KB)

A neural network for implementing the Marcos propagator' bearing estimation algorithm is presented. It is shown both analytically and by simulations that this neural network is guaranteed to be stable and to provide the results arbitrarily close to the accurate propagator operator and the orthogonal projection operator on the noise subspace within an elapsed time of only a few characteristic time... View full abstract»

• ### Neural adaptive sensory processing for undersea sonar

Publication Year: 1992, Page(s):341 - 350
Cited by:  Papers (5)
| | PDF (696 KB)

Neural adaptive beamformers (NABFs) utilize neural paradigms to accomplish desired adaptations that are associated with sensory-field-responsive partitioning and selection processes. Kohonen-type organization and Hopfield-type optimization have been formulated as NABF mechanisms and have been applied to test data. Formulations and results are included. NABFs are also used in conjunction with a lea... View full abstract»

• ### Neural network error corrector for binary messages on hydro-acoustic channels

Publication Year: 1992, Page(s):369 - 375
| | PDF (668 KB)

An application of neural networks for the identification and correction of transmission errors in binary messages is described. The network is used as a classifier of detected hydroacoustic signals. It converts the signals into one of a possible alphabet of symbols. The algorithm used is a Hamming-type neural network classifier associated with the transmission of a Hamming code. This system can de... View full abstract»

• ### Office of Naval Research contributions to neural networks and signal processing in oceanic engineering

Publication Year: 1992, Page(s):299 - 307
Cited by:  Papers (10)
| | PDF (928 KB)

The authors summarize many of the highlights and accomplishments of the Office of Naval Research's neural network basic research programs and share a bit of the historical perspective that serves as a source of pride in the world of Navy science and technology, namely, the long-term support and resulting payoff of Navy-sponsored research in neural networks. The significant problems that must be ov... View full abstract»

• ### A neural approach to the assignment algorithm for multiple-target tracking

Publication Year: 1992, Page(s):326 - 332
Cited by:  Papers (8)
| | PDF (580 KB)

A neural network is presented for performing data association for multiple-target tracking on an optimal assignment basis, i.e., the sum of likelihood functions of measurement-to-track file associations is optimized. The likelihoods are shown to be derivable from a Kalman filter, which updates and maintains the track files from the measurements assigned by the neural network. Not only are measurem... View full abstract»

• ### A neural network based hybrid system for detection, characterization, and classification of short-duration oceanic signals

Publication Year: 1992, Page(s):351 - 363
Cited by:  Papers (42)
| | PDF (1232 KB)

A comprehensive classifier system is presented for short-duration oceanic signals obtained from passive sonar, which exhibit variability in both temporal and spectral characteristics even in signals obtained from the same source. Wavelet-based feature extractors are shown to be superior to the more commonly used autoregressive coefficients and power spectral coefficients for describing these signa... View full abstract»

• ### Extraction of ocean wave parameters from HF backscatter received by a four-element array: analysis and application

Publication Year: 1992, Page(s):376 - 386
Cited by:  Papers (13)
| | PDF (812 KB)

An algorithm that would extend the capabilities of a four-element square array known as the Coastal Oceans Dynamics Applications Radar (CODAR) to include the yielding of directional wave-height spectra from backscattered radiation is addressed. General expressions for the first- and second-order broadbeam radar cross-sections of the ocean surface are applied to the array. A Fourier-basis-function ... View full abstract»

## Aims & Scope

The IEEE Journal of Oceanic Engineering (ISSN 0364-9059) is the online-only quarterly publication of the IEEE Oceanic Engineering Society (IEEE OES). The scope of the Journal is the field of interest of the IEEE OES, which encompasses all aspects of science, engineering, and technology that address research, development, and operations pertaining to all bodies of water. This includes the creation of new capabilities and technologies from concept design through prototypes, testing, and operational systems to sense, explore, understand, develop, use, and responsibly manage natural resources.

Full Aims & Scope

## Meet Our Editors

Editor-in-Chief
N. Ross Chapman
School of Earth & Ocean Sciences
University of Victoria