雷达处理

将深度学习工作流扩展到应用于雷达系统

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.