Radar y redes inalámbricas

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 estimate 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 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).

(Phased Array System Toolbox)

Radar Target Classification Using Machine Learning and Deep Learning

Classify radar returns using machine and deep learning approaches.

(Radar Toolbox)

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.

(Radar Toolbox)