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Proceedings Paper

CFAR detection of small manmade targets using chaotic and statistical CFAR detectors
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Paper Abstract

This paper presents a comparison of chaotic and statistical CFAR detectors for detection of manmade point targets from SAR. Detection of small manmade targets in SAR or IR clutter is an important area of interest in many applications such as ocean surveillance, search and rescue, remote sensing, mine detection, etc. It has been shown that IR and radar clutter exhibit chaotic rather than purely random behavior. From the chaotic point of view, a neural network predictor has been developed using Radial Basis Functions (RBF) to detect small targets embedded in natural clutter. In this paper, we present tradeoff studies between the above chaotic CFAR detector and purely statistical detectors such as the Cell Averaging, Order Statistics, and Optimal Weibull. The tradeoff studies are performed on real data with real or simulated targets. It is shown that adaptive chaotic RBF detectors many outperform statistical detectors in real clutter environments.

Paper Details

Date Published: 4 October 1999
PDF: 13 pages
Proc. SPIE 3809, Signal and Data Processing of Small Targets 1999, (4 October 1999); doi: 10.1117/12.364027
Show Author Affiliations
George A. Lampropoulos, A.U.G. Signals Ltd. (Canada)
Henry Leung, Univ. of Calgary (Canada)

Published in SPIE Proceedings Vol. 3809:
Signal and Data Processing of Small Targets 1999
Oliver E. Drummond, Editor(s)

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