文章基本信息

标题：A Hybrid Approach to Cognition in Radars
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作者：M. Justin Sagayaraj ; Jithesh V. ; J.B. Singh 等
期刊名称：Defence Science Journal
印刷版ISSN：0976-464X
出版年度：2018
卷号：68
期号：2
页码：183-189
语种：English
出版社：Defence Scientific Information & Documentation Centre
摘要：In many engineering domains, cognition is emerging to play vital role. Cognition will play crucial role in radar engineering as well for the development of next generation radars. In this paper, a cognitive architecture for radars is introduced, based on hybrid cognitive architectures. The paper proposes deep learning applications for integrated target classification based on high-resolution radar range profile measurements and target revisit time calculation as case studies. The proposed architecture is based on the artificial cognitive systems concepts and provides a basis for addressing cognition in radars, which is inadequately explored for radar systems. Initial experimental studies on the applicability of deep learning techniques under this approach provided promising results.
其他摘要：In many engineering domains, cognition is emerging to play vital role. Cognition will play crucial role in radar engineering as well for the development of next generation radars. In this paper, a cognitive architecture for radars is introduced, based on hybrid cognitive architectures. The paper proposes deep learning applications for integrated target classification based on high-resolution radar range profile measurements and target revisit time calculation as case studies. The proposed architecture is based on the artificial cognitive systems concepts and provides a basis for addressing cognition in radars, which is inadequately explored for radar systems. Initial experimental studies on the applicability of deep learning techniques under this approach provided promising results.
其他关键词：Cognitive radar;Cognitive architecture;Artificial cognitive system;Convolutional neural network;Long short-term memory - recurrent neural network