A Tailored cGAN SAR Synthetic Data Augmentation Method for ATR Application

• This article proposes a method to simulate Synthetic Aperture Radar (SAR) targets for specific incidence and azimuth angles. Images synthesized by Electromagnetic Computing (EMC) are used to train a Conditional Generative Adversarial Network (cGAN). Two synthetic image chips of the same class and incidence angle, separated by two degrees in azimuth, are used as input to the cGAN. The cGAN predicts the image of the same class and incidence angle whose azimuth angle corresponds to the bisector of the two input chips. An evaluation using the SAMPLE dataset was performed to verify the quality of the image prediction. Running through a total of 100 training epochs, the cGAN converges, reaching the best Mean Squared Error (MSE) after 77 epochs. The results demonstrate that the proposed method is promising for Automatic Target Recognition (ATR) applications. © 2023 IEEE.

Subject headings

TEKNIK OCH TEKNOLOGIER  -- Elektroteknik och elektronik -- Signalbehandling (hsv//swe)

ENGINEERING AND TECHNOLOGY  -- Electrical Engineering, Electronic Engineering, Information Engineering -- Signal Processing (hsv//eng)

Keyword

Automatic Target Recognition

Conditional Generative Adversarial Network

Data Augmentation

Image Translation

Synthetic Aperture Radar

Generative adversarial networks

Mean square error

Radar imaging

Radar target recognition

Augmentation methods

Azimuth angles

Electromagnetics

Incidence angles

Radar target

Synthesised

Synthetic data

Publication and Content Type

ref (subject category)

kon (subject category)