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Detection of Targets Embedded in Sea Ice Clutter by means of MMW Radar Based on Fractal Dimensions, Wavelets, and Neural Classifiers
Chih-ping LIN Motoaki SANO Matsuo SEKINE
IEICE TRANSACTIONS on Communications
Publication Date: 1996/12/25
Print ISSN: 0916-8516
Type of Manuscript: Special Section PAPER (Special Issue on Millimeter-wave Short-range Application Systems Technology)
MMW radar, sea ice, natural classifier, wavelet transform, fractal dimension,
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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.