Agentic AI Based Cognitive Radar for GEOINT Mission

TECHNICAL POINT OF CONTACT (TPOC)
Tom Majumder
PROJECTED CMMC LEVEL REQUIREMENT
Level 2 (Self)
TECHNOLOGY AREAS
Ground Sea
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Sensors
MODERNIZATION PRIORITIES
Integrated Network Systems-of-Systems
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Integrated Sensing and Cyber
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Trusted AI and Autonomy
KEYWORDS
Agentic AI, Cognitive Radar, Synthetic Aperture Radar (SAR)
OBJECTIVE
Develop an agentic AI (artificial intelligence) based cognitive radar system for automatic selection of radar parameters, waveforms, and exploitation for resilient GEOINT applications.
DESCRIPTION
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.
PHASE I
This topic is accepting Direct to Phase II proposals ONLY. To qualify for a Phase II award, proposers must submit Feasibility documentation showing a developed concept for agentic AI based cognitive radar systems. Provide details on the components of the systems and subsystems.The performer must demonstrate capability of the agentic AI based radar system with the detection of interference signals and mitigating these by transmigtting a different signal. This should improve the Signal-to-interference and noise-ration(SINR) <70% to 95% . Also after mitigation image quallaity/resolution should improve from <70% detection and classificvation to 95% detection and classification.
PHASE II
Prototype the agentic AI based cognitive radar system in a modeling and simulation environment. Develop software systems to select various parameters of the radar systems and operating environment. Analyze performance of the system in a testing environment.
PHASE III DUAL USE APPLICATIONS
Develop and build the hardware for the agentic AI based cognitive radar system for technology transition.
REFERENCES
Bell, K.L., Johnson, J.T., Smith, G.E., Baker, C.J., and Rangaswamy, M. Cognitive radar for target tracking using a software defined radar system', 2015 IEEE Radar Conference, pp. 1394-1399
Haykin, S. New generation of radar systems enabled with cognition', IEEE Int. Radar Conference, Arlington, USA, 2010
Guerci, J.R. Cognitive Radar: The Knowledge-Aided Fully Adaptive Approach. Norwood, USA, Artech House, 2010