Optimizing Radar Parameter Values with Language and Genetic Algorithms

Jackson S. Zaunegger, Paul G. Singerman, Ram M. Narayanan, Muralidhar Rangaswamy

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationNAECON 2024 - IEEE National Aerospace and Electronics Conference
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages18-24
Number of pages7
ISBN (Electronic)9798350367621
DOIs
StatePublished - 2024
Event76th Annual IEEE National Aerospace and Electronics Conference, NAECON 2024 - Dayton, United States
Duration: Jul 15 2024Jul 18 2024

Publication series

NameProceedings of the IEEE National Aerospace Electronics Conference, NAECON
ISSN (Print)0547-3578
ISSN (Electronic)2379-2027

Conference

Conference76th Annual IEEE National Aerospace and Electronics Conference, NAECON 2024
Country/TerritoryUnited States
CityDayton
Period7/15/247/18/24

All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Computer Science Applications
  • Control and Systems Engineering
  • Electrical and Electronic Engineering

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