CBRS band experimental waveform dataset for testing radar detection machine learning models

This radar RF waveform dataset (Group6_data) was collected for a tutorial paper that was originally completed in January 2021. The data contained herein was used to demonstrate that the detection accuracy of a spectrogram-based Convolutional Neural Network (CNN) radar detector model is not negatively impacted by the RF hardware impairments experienced due to sending and receiving the data with USRP N210 software defined radios.

The dataset contains 900 examples of CBRS band radar activity and 900 examples of simulated random noise. The IQ data samples were sent/received at 10MSps and are each 80ms in duration. The 900 examples containing radar activity have a random SNR that varies between 10 and 20 dB in 2dB steps.

All 9 of the original (simulated) MATLAB workspaces were generated using the NIST Simulated Radar Waveform Generator that is available here for download:
https://github.com/usnistgov/SimulatedRadarWaveformGenerator

A MATLAB script (included in a separate tar archive file in this dataset) is used to send, or transmit, each of the 9 original simulated waveform batches stored in each MATLAB workspace using a USRP radio at 1.5 MHz over a 2 meter long RF coaxial cable and a 30 dB RF attenuator. A separate receiving MATLAB script (included) is used to receive the entire sent dataset and store it in an "experimental" dataset on the receiving computer.

With these two versions of the radar waveform samples, it is possible to compare the accuracy of a radar detector for both the source and experimental datasets and determine whether the RF hardware impairments imparted by the USRP radio hardware degrades the detector's binary classification accuracy. The following code repository repo contains a set of baseline deep learning radar waveform detection models that were evaluated and documented in our tutorial paper:
https://github.com/usnistgov/BaselineDeepLearningRadarDetectors