레이다 처리

레이다 시스템 응용 분야에서 딥러닝 워크플로 확장

Deep Learning Toolbox™를 Radar Toolbox와 함께 사용하여 레이다 시스템에 딥러닝을 적용합니다. 신호 처리 응용 분야에 대해서는 신호 처리 항목을 참조하십시오.

도움말 항목

Maritime Clutter Suppression with Neural Networks (Radar Toolbox)
Train and evaluate a convolutional neural network to remove clutter returns from maritime radar PPI images using the Deep Learning Toolbox™.
Pedestrian and Bicyclist Classification Using Deep Learning (Radar Toolbox)
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis.
Label Radar Signals with Signal Labeler (Radar Toolbox)
Label the time and frequency features of pulse radar signals with added noise.
Radar Target Classification Using Machine Learning and Deep Learning (Radar Toolbox)
Classify radar returns using machine and deep learning approaches.
Radar and Communications Waveform Classification Using Deep Learning (Radar Toolbox)
Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).
SAR Target Classification Using Deep Learning (Radar Toolbox)
Create and train a simple convolution neural network to classify SAR targets using deep learning.
Automatic Target Recognition (ATR) in SAR Images (Radar Toolbox)
Train a region-based convolutional neural network for target recognition in large-scene synthetic aperture radar images.