2 May 2012 Time series modeling for automatic target recognition
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Abstract
Time series modeling is proposed for identification of targets whose images are not clearly seen. The model building takes into account air turbulence, precipitation, fog, smoke and other factors obscuring and distorting the image. The complex of library data (of images, etc.) serving as a basis for identification provides the deterministic part of the identification process, while the partial image features, distorted parts, irrelevant pieces and absence of particular features comprise the stochastic part of the target identification. The missing data approach is elaborated that helps the prediction process for the image creation or reconstruction. The results are provided.
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Andre Sokolnikov, "Time series modeling for automatic target recognition", Proc. SPIE 8391, Automatic Target Recognition XXII, 839104 (2 May 2012); doi: 10.1117/12.919723; https://doi.org/10.1117/12.919723
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