레이다 처리

LPI Radar Waveform Classification Using Time-Frequency CNN

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

(Phased Array System Toolbox)

Generate Novel Radar Waveforms Using GAN

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

(Radar Toolbox)

Hand Gesture Classification Using Radar Signals and Deep Learning

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

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.

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™.

(Radar Toolbox)

Pedestrian and Bicyclist Classification Using Deep Learning

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

(Radar Toolbox)

Label Radar Signals with Signal Labeler

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

(Radar Toolbox)

Radar Target Classification Using Machine Learning and Deep Learning

Classify radar returns using machine and deep learning approaches.

(Radar Toolbox)

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).

(Radar Toolbox)

SAR Target Classification Using Deep Learning

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

(Radar Toolbox)