AI for Radar

Labeling of radar signals, simulated radar signals to train machine and deep learning models, target classification, signal classification

You can label radar signals using the Signal Labeler app. Augment datasets by simulating radar waveforms and echos for objects with simple geometries such as cylinders and cones. Simulate micro-Doppler signatures of animated objects such as helicopters, pedestrians and bicyclists. Train machine learning and deep learning networks to classify targets and signals.

Topics

Hand Gesture Classification Using Radar Signals and Deep Learning (Deep Learning Toolbox)
Classify ultra-wideband impulse radar signal data using a MISO convolutional neural network.
Human Health Monitoring Using Continuous Wave Radar and Deep Learning (Deep Learning Toolbox)
Reconstruct electrocardiogram signals using a bidirectional long short-term memory network and wavelet multiresolution analysis.
Ship Detection from Sentinel-1 C Band SAR Data Using YOLOX Object Detection (Image Processing Toolbox)
This example shows how to detect ships from Sentinel-1 C Band SAR Data using YOLOX object detection.

Featured Examples

Maritime Clutter Suppression with Neural Networks

Train and evaluate a convolutional neural network to remove clutter returns from maritime radar PPI images using the Deep Learning Toolbox™.

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Label Radar Signals with Signal Labeler

Label the time and frequency features of pulse radar signals with added noise.

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Pedestrian and Bicyclist Classification Using Deep Learning

Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis.

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Radar Target Classification Using Machine Learning and Deep Learning

Classify radar returns using machine and deep learning approaches.

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Radar and Communications Waveform Classification Using Deep Learning

Classify radar and communications waveforms using the Wigner-Ville distribution (WVD) and a deep convolutional neural network (CNN).

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SAR Target Classification Using Deep Learning

Create and train a simple convolution neural network to classify SAR targets using deep learning.

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Automatic Target Recognition (ATR) in SAR Images

Train a region-based convolutional neural network for target recognition in large-scene synthetic aperture radar images.

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LPI Radar Waveform Classification Using Time-Frequency CNN

Train a time-frequency convolutional neural network (CNN) to classify received radar waveforms based on modulation scheme.

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Generate Novel Radar Waveforms Using GAN

Generate new radar waveforms using a Wasserstein generative adversarial network with a gradient penalty (WGAN-GP).

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