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A Hybrid Cognitive System for Radar Monitoring and Control using the Rasmussen Cognition Model

Dr. James Crowder, Systems Fellow, Colorado Engineering Inc.
James Carbone, Department of Electrical and Computer Engineering, Southern Methodist University

The long-term goal of artificial intelligence (AI) is to provide machines the capabilities to learn, think and reason like humans. To achieve these long-term goals, it is necessary to introduce human cognitive-like abilities into AI systems to create truly self-adaptive artificially intelligent systems. This marriage of human cognitive skills with “machines” creates hybrid systems that have characteristics of both. The question becomes, which human cognitive model is appropriate for hybrid artificial intelligent systems. The purpose of this paper is to discuss the development of cognitive models to be infused into a modern radar system to create a Cognitive Radar System (CRS). The notion of a hybrid artificially intelligent system can be divided into two main categories: (a) human-in-the-loop systems with hybrid augmented intelligence requiring human-AI communication/collaboration, and (b) a cognitive computing-based AI in which a fully cognitive model is infused into the machine to allow fully autonomous operation. Here, we discuss the first type, human-in-the-loop cognitive radar systems that provide intelligent decision support and analysis for radar systems. The design of hybrid artificial intelligence methods and algorithms are presented with applications to improvement to modern radar systems, utilizing a Rasmussen Cognition Model (RCM), which we feel is appropriate for a hybrid cognitive system utilized to create a Cognitive Radar System (CRS).

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A Hybrid Cognitive System for Radar Monitoring and Control using the Rasmussen Cognition Model (2 downloads)