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.