레이다 처리
Deep Learning Toolbox™를 Radar Toolbox와 함께 사용하여 레이다 시스템에 딥러닝을 적용합니다. 신호 처리 응용 분야에 대해서는 신호 처리 항목을 참조하십시오.
추천 예제
Improving Weather Radar Moment Estimation with Convolutional Neural Networks
Train and evaluate convolutional neural networks (CNN) to improve weather radar moment estimation.
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.
Generate Novel Radar Waveforms Using GAN
Generate new radar waveforms using a Wasserstein generative adversarial network with a gradient penalty (WGAN-GP).
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™.
Pedestrian and Bicyclist Classification Using Deep Learning
Classify pedestrians and bicyclists based on their micro-Doppler characteristics using deep learning and time-frequency analysis.
Label Radar Signals with Signal Labeler
Label the time and frequency features of pulse radar signals with added noise.
Radar Target Classification Using Machine Learning and Deep Learning
Classify radar returns using machine and deep learning approaches.
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).
SAR Target Classification Using Deep Learning
Create and train a simple convolution neural network to classify SAR targets using deep learning.
