Study on the Functional Architecture and Improvement Accuracy for Auto Target Classification on the SAR Image by using CNN Ensemble Model based on the Radar System for the Fighter

The fighter pilot uses radar mounted on the fighter to obtain high-resolution SAR (Synthetic Aperture Radar) images for a specific area of distance, and then the pilot visually classifies targets within the image. However, the target configuration captured in the SAR image is relatively small in size, and distortion of that type occurs depending on the depression angle, making it difficult for pilot to classify the type of target. Also, being present with various types of clutters, there should be errors in target classification and pilots should be even worse if tasks such as navigation and situational awareness are carried out simultaneously. In this paper, the concept of operation and functional structure of radar system for fighter jets were presented to transfer the SAR image target classification task of fighter pilots to radar system, and the method of target classification with high accuracy was studied using the CNN ensemble model to archive higher classification accuracy than single CNN model.

전투기용 레이다 기반 SAR 영상 자동표적분류 기능 구조 및 CNN 앙상블 모델을 이용한 표적분류 정확도 향상 방안 연구