Literature DB >> 30189393

Mapping radiation distribution on ground based on the measurement using an unmanned aerial vehicle.

Shuangyue Zhang1, Ruirui Liu2, Tianyu Zhao3.   

Abstract

Mapping the radiation distribution on ground during a radiological emergency monitoring, or decontamination mission is an important task. Accurate knowledge of the radioactivity distribution can help radiation workers locate the contamination, which reduces unnecessary radiation exposure to personnel and perhaps to some extent also the exposure to the public. Recently, radiation monitoring systems based on unmanned aerial vehicles (UAV) have been widely studied and employed. However, development of algorithms for mapping the contamination from measured data obtained by the detection system mounted on an UAV is still lacking. In this work, we implemented an advanced statistical reconstruction algorithm for mapping spread and point radiation contamination. The algorithm significantly improves accuracy in the scope and location of radiation contamination.
Copyright © 2018 Elsevier Ltd. All rights reserved.

Keywords:  Decontamination monitoring; Nuclear emergency monitoring; Radiation contamination mapping; Radiological emergency monitoring; Reconstruction algorithm; Unmanned aerial vehicle

Mesh:

Substances:

Year:  2018        PMID: 30189393     DOI: 10.1016/j.jenvrad.2018.08.016

Source DB:  PubMed          Journal:  J Environ Radioact        ISSN: 0265-931X            Impact factor:   2.674


  3 in total

1.  U-Space and UTM Deployment as an Opportunity for More Complex UAV Operations Including UAV Medical Transport.

Authors:  Mateusz Kotlinski; Justyna Krol Calkowska
Journal:  J Intell Robot Syst       Date:  2022-08-24       Impact factor: 3.129

2.  The Use of Drones in the Area of Minimizing Health Risk during the COVID-19 Epidemic.

Authors:  Esthera Justyna Król-Całkowska; Daniel Walczak
Journal:  J Intell Robot Syst       Date:  2022-09-30       Impact factor: 3.129

3.  Application and Communication Optimization Technology of Unmanned Distribution Car under Deep Learning in Logistics Express of COVID-19.

Authors:  Xinyue Song; Fengkai Luan
Journal:  Comput Intell Neurosci       Date:  2022-09-17
  3 in total

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