Extending language-based cost functions with deep learning

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

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

2 Scopus citations

Abstract

Cognitive radar systems are radar systems that can self-adjust themselves to respond to changes in the environment. Developing cognitive radar systems relies on their ability to detect these changes in operational conditions and use this knowledge to change the operating characteristics of the system, to optimally solve a selected task. Engineers must have an expert level knowledge of radar systems in order to solve these problems as they arise. The goals of the system can be easily stated to engineers in the form of natural language, but are very difficult for computers to analyze. Previous work has shown that Natural Language Processing (NLP) models can be developed to extract radar parameters, values, and units from text. Language Based Cost Functions (LBCFs) can then utilize this extracted information to develop constraints on specific r adar p arameters. I n t his work, we propose to combine these language models with LBCFs to define a objective function for optimization tasks using natural language.

Original languageEnglish (US)
Title of host publicationRadar Sensor Technology XXVIII
EditorsAbigail S. Hedden, Gregory J. Mazzaro
PublisherSPIE
ISBN (Electronic)9781510674141
DOIs
StatePublished - 2024
EventRadar Sensor Technology XXVIII 2024 - National Harbor, United States
Duration: Apr 22 2024Apr 24 2024

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume13048
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceRadar Sensor Technology XXVIII 2024
Country/TerritoryUnited States
CityNational Harbor
Period4/22/244/24/24

All Science Journal Classification (ASJC) codes

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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