Radiometric Passive Imaging for Robust Concealed Object Identification

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Artificial Intelligence (AI) based millimeter wave radiometric imaging has become popular in a wide range of public security check systems, such as concealed object detection and identification. However, the low radiometric temperature contrast between small objects and low sensitivity is restricted to some extent. In this paper, an advanced radiometric passive imaging simulation model is proposed to improve the radiometric temperature contrast. This model considers additional noise, such as blur, variation in sensors, noise sources and summation of the number of frames. We establish a comprehensive training dataset that considers the physical characteristics of concealed objects. It can effectively fill the lack of a large database to avoid deteriorating the identification accuracy of AI applications. Moreover, it is also a key solution for improving the robustness of AI based object identification by using a convolutional neural network (CNN). Finally, simulation results are presented and analyzed to validate the proposed comprehensive training dataset and simulation model. Consequently, the proposed simulation model can effectively improve the robustness and accuracy of AI-based concealed object identification.

Original languageEnglish
Title of host publication2020 IEEE Radar Conference, RadarConf 2020
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728189420
Publication statusPublished - 2020 Sept 21
Event2020 IEEE Radar Conference, RadarConf 2020 - Florence, Italy
Duration: 2020 Sept 212020 Sept 25

Publication series

NameIEEE National Radar Conference - Proceedings
ISSN (Print)1097-5659


Conference2020 IEEE Radar Conference, RadarConf 2020


  • Concealed Object Identification
  • Passive Imaging
  • Radiometric

ASJC Scopus subject areas

  • Electrical and Electronic Engineering


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