Radarverarbeitung

Erweitern von Deep-Learning-Workflows mit Radarsystemanwendungen

Wenden Sie Deep Learning auf Radarsysteme an, indem Sie die Deep Learning Toolbox™ zusammen mit der Radar Toolbox verwenden. Für Anwendungen der Signalverarbeitung siehe Signalverarbeitung.

Themen

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.

Enthaltene Beispiele

Hand Gesture Classification Using Radar Signals and Deep Learning

Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network.

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Human Health Monitoring Using Continuous Wave Radar and Deep Learning

Reconstruct electrocardiogram signals using a bidirectional long short-term memory network and wavelet multiresolution analysis.

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