Heat-seeking missiles continue to be serious threats to aircrafts. In recent years, open-loop DIRCM systems have proven to be efficient countermeasures against these missiles. However, closed-loop DIRCM systems seem to be more promising as they employ a jamming code based on the classification or identification of an incoming missile through retro-reflection from the seeker head. In these systems, the retro-reflected beam is influenced by the optical turbulence in both transmission and return paths. In this paper, the influence of optical turbulence on the identification performance of a closed-loop DIRCM system is investigated. A dataset is created by varying the seeker spin and carrier frequencies along with the optical turbulence levels and range. Deep neural network classifiers were trained on this dataset and evaluated in terms of their effectiveness in identifying missile seekers with the DIRCM system.
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