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:<br> 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:<br> https://github.com/usnistgov/BaselineDeepLearningRadarDetectors本雷达射频（Radio Frequency, RF）波形数据集（Group6_data）是为一篇于2021年1月完成的教程论文采集所得。本数据集收录的数据用于验证：基于语谱图的卷积神经网络（Convolutional Neural Network, CNN）雷达检测模型的检测精度，不会因使用USRP N210软件定义无线电收发数据所产生的射频硬件损伤而出现劣化。该数据集包含900例CBRS频段雷达活动样本与900例模拟随机噪声样本。IQ数据采样率为10MSps，单条样本时长均为80ms。其中900例雷达活动样本的信噪比（Signal-to-Noise Ratio, SNR）随机取值于10dB至20dB之间，步长为2dB。全部9份原始（模拟）MATLAB工作区文件，均通过可在此处下载的美国国家标准与技术研究院（National Institute of Standards and Technology, NIST）模拟雷达波形生成器生成：<br> https://github.com/usnistgov/SimulatedRadarWaveformGenerator 本数据集附带的MATLAB脚本（存放于独立tar归档文件中），用于通过USRP无线电，以1.5MHz带宽、经由2米长射频同轴电缆与30dB射频衰减器，发送存储于每份MATLAB工作区中的9组原始模拟波形批次。附带的另一接收端MATLAB脚本，用于接收完整的发送数据集，并将其存储于接收计算机的“实验性”数据集中。依托这两套雷达波形样本（原始与实验数据集），可对比雷达检测器在两类数据集上的检测精度，进而判断USRP无线电硬件引入的射频硬件损伤是否会降低检测器的二分类精度。以下代码仓库包含了本教程论文中评估并记载的基线深度学习雷达波形检测模型集：<br> https://github.com/usnistgov/BaselineDeepLearningRadarDetectors