Extending language-based cost functions with deep learning

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

doi:10.1117/12.3013555

7 June 2024 Extending language-based cost functions with deep learning

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

Proceedings Volume 13048, Radar Sensor Technology XXVIII; 130480J (2024) https://doi.org/10.1117/12.3013555
Event: SPIE Defense + Commercial Sensing, 2024, National Harbor, Maryland, United States

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 radar parameters. In this work, we propose to combine these language models with LBCFs to define a objective function for optimization tasks using natural language.

Conference Presentation

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jackson S. Zaunegger, Paul G. Singerman, Ram M. Narayanan, and Muralidhar Rangaswamy "Extending language-based cost functions with deep learning", Proc. SPIE 13048, Radar Sensor Technology XXVIII, 130480J (7 June 2024); https://doi.org/10.1117/12.3013555

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