雷达处理
R2026b通过将 Deep Learning Toolbox™ 与 Radar Toolbox 结合使用，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。
精选示例
Novel Drone and Bird Micro-Doppler Dataset for Pattern Discovery and Machine Learning
Explore a novel dataset that captures the micro-Doppler signatures of five drones and five birds, each with two different frequency modulated continuous wave (FMCW) radars.
CBRS Band Radar Detection in 5G Signals and Noise Using YOLOX
Detect rectangular and linear-FM radar pulse waveforms embedded in a 5G+noise environment using a combination of time-frequency maps and a deep learning object detector.
From ADC to AI: A Radar Data Deep Learning Tutorial
Implement a full deep learning workflow for a radar system, including how to process raw ADC data, create a labeled dataset, and train a neural network on radar data.
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 convolutional neural network (CNN) to classify SAR targets using deep learning.
