FDFD Simulations

Citation Author(s):
Mahshid
Asri
Northeastern University
Ann
Morgenthaler
Northeastern University
Carey
Rappaport
Northeastern University
Submitted by:
Mahshid Asri
Last updated:
Mon, 11/04/2024 - 14:36
DOI:
10.21227/r8td-a279
Data Format:
License:
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Abstract 

This paper presents a deep learning model for fast and accurate radar detection and pixel-level localization of large concealed metallic weapons on pedestrians walking along a sidewalk. The considered radar is stationary, with a multi-beam antenna operating at 30 GHz with 6 GHz bandwidth. A large modeled data set has been generated by running 2155 2D-FDFD simulations of torso cross sections of persons walking toward the radar in various scenarios. 

Instructions: 

This data includes two folders. The folder "Images" contains the reconstructed radar images for all the simulated case. Each simulated case contains 4 separate images:

image_casenumber_3.png corresponds to the reconstruced image when the radar focuses on the left arm

image_casenumber_2.png corresponds to the reconstruced image when the radar focuses on the right arm

image_casenumber_1.png corresponds to the reconstruced image when the radar focuses on the middle of the torso

image_casenumber_0.png is generated by combining the images above in a complex format (using their amplitude and phase).

 The folder "Masks" contains the ground truth files used for each simulation. The 4 ground truth images corresponding to each case are exactly alike and are added for making the training part easier. 

Funding Agency: 
U.S. Department of Homeland Security, Science and Technology Directorate
Grant Number: 
22STESE00001-02-00

Dataset Files

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