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Proceedings Paper

Adaptive multispectral detection of small targets using spatial and spectral convergence factor
Author(s): Laurent Nolibe; Julien Borgnino; Marc Ducoulombier; Michel Artaud
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Paper Abstract

The main infrared search and track systems (IRST) purpose is to realize optimal discrimination between true targets and background clutter (false alarm). In such single band systems, two dimensional least mean square (TDLMS) adaptive filter achieve good results in small target detection. However, detection performance are strongly dependent on background correlation length. When difference between background and target correlation length is too small, performance detection decreases. The method presented in this paper is applied to a naval dual band panoramic surveillance system for target detection at low elevation angles. It consists in adjusting the time-varying convergence factor of TDLMS filter, not only by using spatial statistics, but also by integrating a local spectral parameter. The use of this information is based on theoretical spectral radiance discrimination in LWIR and MWIR bands, between targets and backgrounds. When the local spectral parameter matches spectral background response, the filter reactivity is optimum via the spatial convergence factor, whereas it is decreased in the presence of spectral target characteristics. We achieve in this way a better target-to-clutter discrimination and independently of background correlation length.

Paper Details

Date Published: 31 May 1996
PDF: 10 pages
Proc. SPIE 2759, Signal and Data Processing of Small Targets 1996, (31 May 1996); doi: 10.1117/12.241161
Show Author Affiliations
Laurent Nolibe, DCN Ctr. Technique des Systemes Navals (France)
Julien Borgnino, Univ. de Nice-Sophia Antipolis (France)
Marc Ducoulombier, DCN Ctr. Technique des Systemes Navals (France)
Michel Artaud, DCN Ctr. Technique des Systemes Navals (France)

Published in SPIE Proceedings Vol. 2759:
Signal and Data Processing of Small Targets 1996
Oliver E. Drummond, Editor(s)

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