TY - GEN
T1 - Optimizing Radar Parameter Values with Language and Genetic Algorithms
AU - Zaunegger, Jackson S.
AU - Singerman, Paul G.
AU - Narayanan, Ram M.
AU - Rangaswamy, Muralidhar
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - Cognitive radar systems are radar systems that are capable of adjusting their operating parameters in response to perceived changes in their environment. The development of these systems requires them to detect these changes, and possess the knowledge to use this information to adjust their operating characteristics. The system must understand the task it is trying to complete so it may determine the best way to accomplish it. These goals may be stated to the computational system in the form of textual inputs. We have previously shown that Natural Language Processing (NLP) models can be used to extract radar parameters, values, and units from text. We have also shown that these models may be used to extend the capabilities of Language Based Cost Functions (LBCFs). In this work, we show how these NLP models and LBCFs may be used to develop a fitness function for a Genetic Algorithm (GA), and to find the optimal set of radar parameter values to achieve a given task. This fitness function may also be used to train Reinforcement Learning (RL) based systems.
AB - Cognitive radar systems are radar systems that are capable of adjusting their operating parameters in response to perceived changes in their environment. The development of these systems requires them to detect these changes, and possess the knowledge to use this information to adjust their operating characteristics. The system must understand the task it is trying to complete so it may determine the best way to accomplish it. These goals may be stated to the computational system in the form of textual inputs. We have previously shown that Natural Language Processing (NLP) models can be used to extract radar parameters, values, and units from text. We have also shown that these models may be used to extend the capabilities of Language Based Cost Functions (LBCFs). In this work, we show how these NLP models and LBCFs may be used to develop a fitness function for a Genetic Algorithm (GA), and to find the optimal set of radar parameter values to achieve a given task. This fitness function may also be used to train Reinforcement Learning (RL) based systems.
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U2 - 10.1109/NAECON61878.2024.10670614
DO - 10.1109/NAECON61878.2024.10670614
M3 - Conference contribution
AN - SCOPUS:85204968340
T3 - Proceedings of the IEEE National Aerospace Electronics Conference, NAECON
SP - 18
EP - 24
BT - NAECON 2024 - IEEE National Aerospace and Electronics Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 76th Annual IEEE National Aerospace and Electronics Conference, NAECON 2024
Y2 - 15 July 2024 through 18 July 2024
ER -