雷达处理
通过结合使用 Deep Learning Toolbox™ 和 Radar Toolbox,将深度学习应用于雷达系统。有关信号处理应用,请参阅信号处理。
主题
- 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.