Investigating the application of deep learning for electromagnetic simulation prediction

Steven R. Price; Stanton R. Price; Carey D. Price; Clay B. Blount

doi:10.1117/12.2305030

4 May 2018 Investigating the application of deep learning for electromagnetic simulation prediction

Steven R. Price, Stanton R. Price, Carey D. Price, Clay B. Blount

Proceedings Volume 10633, Radar Sensor Technology XXII; 106331C (2018) https://doi.org/10.1117/12.2305030
Event: SPIE Defense + Security, 2018, Orlando, FL, United States

Abstract

Applications seeking to exploit electromagnetic scattering characteristics of an imaging or detection problem typically require a large number of electromagnetic simulations. Because these simulations are often computationally intensive, valuable resources are required to perform the simulations in an efficient and timely manner, which is not always freely available or accessible. In this work, we investigate the utility of deep learning for electromagnetic simulation prediction. Specifically, we explore using artificial neural networks to learn the connection between a generic object and its resulting bistatic radar cross section, thereby removing the need to repeatedly perform timely simulations. Such a system would be trained in an offline setting and consequently enable rapid bistatic radar cross section predictions for new objects in the future. While deep learning can be seen as a computationally expensive technique, this cost is only experienced during the training of the system and not subsequently in the acquisition of results. The goal of this work is to learn the applicability of deep learning for electromagnetic simulation prediction as well as its potential limitations. Several simple objects are investigated and a thorough statistical analysis will be used to assess the performance of our proposed method.

Citation Download Citation

Steven R. Price, Stanton R. Price, Carey D. Price, and Clay B. Blount "Investigating the application of deep learning for electromagnetic simulation prediction", Proc. SPIE 10633, Radar Sensor Technology XXII, 106331C (4 May 2018); https://doi.org/10.1117/12.2305030

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available