Baseline Deep Learning Detectors for Radar Detection in the 3.5 GHz CBRS Band
Resources
13 resources available
-
DOI Access for Baseline Deep Learning Detectors for Radar Detection in the 3.5 GHz CBRS Band
FILE -
CNN3-SpectroMaxHold_model
APPLICATION/OCTET-STREAM -
MobileNetV2-SpectroMaxHold_model
APPLICATION/OCTET-STREAM -
ResNet50-SpectroMaxHold_model
APPLICATION/OCTET-STREAM -
Xception-SpectroMaxHold_model
APPLICATION/OCTET-STREAM -
CNN4-Spectro_model
APPLICATION/OCTET-STREAM -
CNN5-Spectro_model
APPLICATION/OCTET-STREAM -
SHA256 File for CNN3-SpectroMaxHold_model
TEXT/PLAIN -
SHA256 File for CNN4-Spectro_model
TEXT/PLAIN -
SHA256 File for CNN5-Spectro_model
TEXT/PLAIN -
SHA256 File for MobileNetV2-SpectroMaxHold_model
TEXT/PLAIN -
SHA256 File for ResNet50-SpectroMaxHold_model
TEXT/PLAIN -
SHA256 File for Xception-SpectroMaxHold_model
TEXT/PLAIN
Complete Metadata
| @type | dcat:Dataset |
|---|---|
| accessLevel | public |
| bureauCode |
[
"006:55"
]
|
| contactPoint |
{
"fn": "Raied Caromi",
"hasEmail": "mailto:raied.caromi@nist.gov"
}
|
| description | This project aims to create a comprehensive framework for generating radio frequency (RF) datasets, designing deep learning (DL) detectors, and evaluating their detection performance using both simulated and experimental test data. The proposed tools and techniques are developed in the context of dynamic spectrum use for the 3.5 GHz Citizens Broadband Radio Service (CBRS), but they can be utilized and expanded for standardization of machine learned spectrum awareness technologies and methods. This dataset consists of pre-trained DL models for radar detection in the CBRS band using simulated waveforms. The code for creating and using these models is available at https://github.com/usnistgov/BaselineDeepLearningRadarDetectors. |
| distribution |
[
{
"title": "DOI Access for Baseline Deep Learning Detectors for Radar Detection in the 3.5 GHz CBRS Band",
"accessURL": "https://doi.org/10.18434/mds2-2380"
},
{
"title": "CNN3-SpectroMaxHold_model",
"mediaType": "application/octet-stream",
"description": "Spectrogram max-hold pretrained model (CNN3)",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/CNN3-SpectroMaxHold_model_V1.h5"
},
{
"title": "MobileNetV2-SpectroMaxHold_model",
"mediaType": "application/octet-stream",
"description": "Spectrogram max-hold pretrained model (MobileNetV2)",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/MobileNetV2-SpectroMaxHold_model_V1.h5"
},
{
"title": "ResNet50-SpectroMaxHold_model",
"mediaType": "application/octet-stream",
"description": "Spectrogram max-hold pretrained model (ResNet50)",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/ResNet50-SpectroMaxHold_model_V1.h5"
},
{
"title": "Xception-SpectroMaxHold_model",
"mediaType": "application/octet-stream",
"description": "Spectrogram max-hold pretrained model (Xception)",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/Xception-SpectroMaxHold_model_V1.h5"
},
{
"title": "CNN4-Spectro_model",
"mediaType": "application/octet-stream",
"description": "Spectrogram pretrained model (CNN4)",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/CNN4-Spectro_model_V1.h5"
},
{
"title": "CNN5-Spectro_model",
"mediaType": "application/octet-stream",
"description": "Spectrogram pretrained model (CNN5)",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/CNN5-Spectro_model_V1.h5"
},
{
"title": "SHA256 File for CNN3-SpectroMaxHold_model",
"mediaType": "text/plain",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/CNN3-SpectroMaxHold_model_V1.h5.sha256"
},
{
"title": "SHA256 File for CNN4-Spectro_model",
"mediaType": "text/plain",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/CNN4-Spectro_model_V1.h5.sha256"
},
{
"title": "SHA256 File for CNN5-Spectro_model",
"mediaType": "text/plain",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/CNN5-Spectro_model_V1.h5.sha256"
},
{
"title": "SHA256 File for MobileNetV2-SpectroMaxHold_model",
"mediaType": "text/plain",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/MobileNetV2-SpectroMaxHold_model_V1.h5.sha256"
},
{
"title": "SHA256 File for ResNet50-SpectroMaxHold_model",
"mediaType": "text/plain",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/ResNet50-SpectroMaxHold_model_V1.h5.sha256"
},
{
"title": "SHA256 File for Xception-SpectroMaxHold_model",
"mediaType": "text/plain",
"downloadURL": "https://data.nist.gov/od/ds/mds2-2380/Xception-SpectroMaxHold_model_V1.h5.sha256"
}
]
|
| identifier | ark:/88434/mds2-2380 |
| issued | 2021-04-05 |
| keyword |
[
"3.5 GHz; CBRS; radar; detection; deep learning; radio frequency signals; spectrum; MLSA"
]
|
| landingPage | https://data.nist.gov/od/id/mds2-2380 |
| language |
[
"en"
]
|
| license | https://www.nist.gov/open/license |
| modified | 2021-03-01 00:00:00 |
| programCode |
[
"006:045"
]
|
| publisher |
{
"name": "National Institute of Standards and Technology",
"@type": "org:Organization"
}
|
| theme |
[
"Advanced Communications:Wireless (RF)"
]
|
| title | Baseline Deep Learning Detectors for Radar Detection in the 3.5 GHz CBRS Band |
