IEICE TRANSACTIONS on Communications
Detection of Targets Embedded in Sea Ice Clutter by means of MMW Radar Based on Fractal Dimensions, Wavelets, and Neural Classifiers
Summary :
The millimeter wave (MMW) radar has good compromise characteristics of both microwave radar and optical sensors. It has better angular and range resolving abilities than microwave radar, and a longer penetrating range than optical sensors. We used the MMW radar to detect targets located in the sea and among sea ice clutter based on fractals, wavelets, and neural networks. The wavelets were used as feature extractors to decompose the MMW radar images and to extract the feature vectors from approximation signals at different resolution levels. Unsupervised neural classifiers with parallel computational architecture were used to classify sea ice, sea water and targets based on the competitive learning algorithm. The fractal dimensions could provide a quantitative description of the roughness of the radar image. Using these techniques, we can detect targets quickly and clearly discriminate between sea ice, sea water, and targets.
- Publication
- IEICE TRANSACTIONS on Communications Vol.E79-B No.12 pp.1818-1826
- Publication Date
- 1996/12/25
- Publicized
- Online ISSN
- DOI
- Type of Manuscript
- Special Section PAPER (Special Issue on Millimeter-wave Short-range Application Systems Technology)
- Category
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Chih-ping LIN, Motoaki SANO, Matsuo SEKINE, "Detection of Targets Embedded in Sea Ice Clutter by means of MMW Radar Based on Fractal Dimensions, Wavelets, and Neural Classifiers" in IEICE TRANSACTIONS on Communications,
vol. E79-B, no. 12, pp. 1818-1826, December 1996, doi: .
Abstract: The millimeter wave (MMW) radar has good compromise characteristics of both microwave radar and optical sensors. It has better angular and range resolving abilities than microwave radar, and a longer penetrating range than optical sensors. We used the MMW radar to detect targets located in the sea and among sea ice clutter based on fractals, wavelets, and neural networks. The wavelets were used as feature extractors to decompose the MMW radar images and to extract the feature vectors from approximation signals at different resolution levels. Unsupervised neural classifiers with parallel computational architecture were used to classify sea ice, sea water and targets based on the competitive learning algorithm. The fractal dimensions could provide a quantitative description of the roughness of the radar image. Using these techniques, we can detect targets quickly and clearly discriminate between sea ice, sea water, and targets.
URL: https://global.ieice.org/en_transactions/communications/10.1587/e79-b_12_1818/_p
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@ARTICLE{e79-b_12_1818,
author={Chih-ping LIN, Motoaki SANO, Matsuo SEKINE, },
journal={IEICE TRANSACTIONS on Communications},
title={Detection of Targets Embedded in Sea Ice Clutter by means of MMW Radar Based on Fractal Dimensions, Wavelets, and Neural Classifiers},
year={1996},
volume={E79-B},
number={12},
pages={1818-1826},
abstract={The millimeter wave (MMW) radar has good compromise characteristics of both microwave radar and optical sensors. It has better angular and range resolving abilities than microwave radar, and a longer penetrating range than optical sensors. We used the MMW radar to detect targets located in the sea and among sea ice clutter based on fractals, wavelets, and neural networks. The wavelets were used as feature extractors to decompose the MMW radar images and to extract the feature vectors from approximation signals at different resolution levels. Unsupervised neural classifiers with parallel computational architecture were used to classify sea ice, sea water and targets based on the competitive learning algorithm. The fractal dimensions could provide a quantitative description of the roughness of the radar image. Using these techniques, we can detect targets quickly and clearly discriminate between sea ice, sea water, and targets.},
keywords={},
doi={},
ISSN={},
month={December},}
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TY - JOUR
TI - Detection of Targets Embedded in Sea Ice Clutter by means of MMW Radar Based on Fractal Dimensions, Wavelets, and Neural Classifiers
T2 - IEICE TRANSACTIONS on Communications
SP - 1818
EP - 1826
AU - Chih-ping LIN
AU - Motoaki SANO
AU - Matsuo SEKINE
PY - 1996
DO -
JO - IEICE TRANSACTIONS on Communications
SN -
VL - E79-B
IS - 12
JA - IEICE TRANSACTIONS on Communications
Y1 - December 1996
AB - The millimeter wave (MMW) radar has good compromise characteristics of both microwave radar and optical sensors. It has better angular and range resolving abilities than microwave radar, and a longer penetrating range than optical sensors. We used the MMW radar to detect targets located in the sea and among sea ice clutter based on fractals, wavelets, and neural networks. The wavelets were used as feature extractors to decompose the MMW radar images and to extract the feature vectors from approximation signals at different resolution levels. Unsupervised neural classifiers with parallel computational architecture were used to classify sea ice, sea water and targets based on the competitive learning algorithm. The fractal dimensions could provide a quantitative description of the roughness of the radar image. Using these techniques, we can detect targets quickly and clearly discriminate between sea ice, sea water, and targets.
ER -
English
