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