Target Detection in Changing Noisy Environment Using Coherent Radar Model Integrated with Soar Cognitive Architecture

Abstract

The key abilities of the cognitive radar system include learning new knowledge about its surroundings, critical decision-making, and adaptation to a constantly changing environment. Among others, cognitive radar aims to achieve optimal radar resource management and maximize target detection performance. Radar systems often have to operate in strongly interfered environments. This work investigates the novel research direction of cognitive radar system design based on artificial intelligence (AI) cognitive architectures. Namely, the integration of a coherent radar model with Soar cognitive architecture has been developed. This work describes in detail the sensorimotor subsystem, which represents radar signal processing algorithms and waveform design. The AI subsystem comprising of the long-term and short-term memories of the Soar architecture was also characterized. Cognitive memories are designed to represent radar environment state and solve signal-to-noise-ratio (SNR) maximization problem. Using computer simulations it has been shown that the proposed system improved target detection performance in comparison to traditional radar system.