Abstract
This paper considers a new cognitive radar architecture based on artificial intelligence cognitive architectures (CAs). The CAs are the computational embodiments of the theory about modeling the human mind used in artificial intelligence. They found applications in, e.g., robotics and autonomous vehicles. However, the research related to the topic of integrating artificial intelligence CAs with radar systems is very limited. In this paper, a novel cognitive radar architecture has been proposed. Cognitive abilities such as learning, perception, attention, and decision-making mechanisms were conformed to radar capabilities. This new concept introduces promising visual attention mechanisms for target prioritization, declarative memories for a broader understanding of the radar environment, and reinforcement learning to improve the signal-to-noise ratio. All of the components mentioned above, combined together, look promising to improve an overall radar performance.
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