Agentic AI Based Cognitive Radar for GEOINT Mission

Agentic AI enables autonomous control of expert systems for desired information gathering or completing a task. Compared to the general AI systems, an agentic AI system can make its decision by sensing the environment autonomously without relying on human input. Hence, agentic AI is desired for many expert systems such as self-driving car (Tesla) and many other systems. In this research, we are seeking development of an agentic AI based cognitive radar system for next generation, resilient radar system. Once initiated, agentic AI enabled radar will sense the environment for operating conditions such as interference, clutter, and signal to noise ratio. Based on this information, the system will learn (i.e., cognition process) to transmit optimum waveforms with desired frequency, bandwidth, pulse repletion frequency (PRF) and other critical parameters. This will enable future GEOINT capabilities to be resilient in the presence of asymmetric operating environments that adversaries may impose to significantly reducing our GEOINT mission.