@inproceedings{f000a382044049a7b93707155ebb60c3,
title = "Extending language-based cost functions with deep learning",
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.",
author = "Zaunegger, {Jackson S.} and Singerman, {Paul G.} and Narayanan, {Ram M.} and Muralidhar Rangaswamy",
note = "Publisher Copyright: {\textcopyright} 2024 SPIE.; Radar Sensor Technology XXVIII 2024 ; Conference date: 22-04-2024 Through 24-04-2024",
year = "2024",
doi = "10.1117/12.3013555",
language = "English (US)",
series = "Proceedings of SPIE - The International Society for Optical Engineering",
publisher = "SPIE",
editor = "Hedden, {Abigail S.} and Mazzaro, {Gregory J.}",
booktitle = "Radar Sensor Technology XXVIII",
address = "United States",
}