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Radars et télécommunications
Classifiez les cibles radar et les formes d’onde avec des modèles de Deep Learning. Identifiez les piétons et les cyclistes. Utilisez des réseaux de neurones graphiques pour allouer les ressources de télécommunications.
Informations connexes
Sélection d՚exemples
Automated Labeling of Time-Frequency Regions for AI-Based Spectrum Sensing Applications
Use rule-based methods or unsupervised learning techniques to help automate time-frequency data labeling.
Export Labeled Data from Signal Labeler for AI-Based Spectrum Sensing Applications
Use deep learning networks and the Signal Labeler app to identify frames from the Bluetooth® and Wi-Fi® wireless standards.
Wireless Resource Allocation Using Graph Neural Network
Use graph neural networks for power allocation in wireless networks.
CBRS Band Radar Parameter Estimation Using YOLOX
Detect radar pulses in noise and estimates the pulse parameters using a combination of time-frequency maps and a deep-learning object detector.
Direction-of-Arrival Estimation Using Deep Learning
Estimate direction of arrival using deep learning by predicting angular directions directly from the sample covariance matrix.
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 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 Target Classification Using Machine Learning and Deep Learning
Classify radar returns using machine and deep learning approaches.
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.
