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- W2034383970 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.© (1996) COPYRIGHT SPIE--The International Society for Optical Engineering. Downloading of the abstract is permitted for personal use only." @default.
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- W2034383970 date "1996-05-31" @default.
- W2034383970 modified "2023-09-23" @default.
- W2034383970 title "<title>Adaptive multispectral detection of small targets using spatial and spectral convergence factor</title>" @default.
- W2034383970 doi "https://doi.org/10.1117/12.241161" @default.
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