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Sensing spectrum sharing based massive MIMO radar for drone tracking and interception
Radar sensors are becoming crucial for environmental perception in a world with the tremendous growth of unmanned aerial vehicles (UAVs) or drones. When public safety is a concern, the localization of drones are of great significance. However, a drone used for a wrong motive can cause a serious prob...
Autores principales: | , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Public Library of Science
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122215/ https://www.ncbi.nlm.nih.gov/pubmed/35594276 http://dx.doi.org/10.1371/journal.pone.0268834 |
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author | Junior, Milembolo Miantezila Guo, Bin |
author_facet | Junior, Milembolo Miantezila Guo, Bin |
author_sort | Junior, Milembolo Miantezila |
collection | PubMed |
description | Radar sensors are becoming crucial for environmental perception in a world with the tremendous growth of unmanned aerial vehicles (UAVs) or drones. When public safety is a concern, the localization of drones are of great significance. However, a drone used for a wrong motive can cause a serious problem for the environment and public safety, given the fact that the dynamic movement of a drone’s emission signal and location tracking is different from existing positioning. This study proposes a safety zone characterized by the presence of N radars sensors with a goal to track and destabilized rogue drones attending to penetrate safety zones (stadium and school). Specifically, a new joint estimation based on a Gaussian filter has been introduced for spectrum sharing and detection awareness. The profit of this novel sensing method can be clearly seen when the two joint hidden states are taken into consideration. Therefore, the drone’s emission state is analyzed by estimating its movement jointly. Considering the drone’s unknown states and actual positioning, an algorithm is developed based on dynamic states space model. Where Bernoulli filter model is designed to estimate recursively the unknown stages of the drone and its changing location based on time. Meanwhile a power control acted from the radar to the targeted drones so that rogue drones are optimally tracked and destabilized over time. Furthermore, an expanding mechanism has been generated to accurately track the drone and enhance detection. A thoughtful result of the experimentation shows clearly that, even when the drone is moving, spectral detection can be performed accurately by chasing its positions. Its demonstrates at 90% of credibility that the original signal has a direct effect on the propagated signal. Therefore, the magnitude of the Doppler shift increases with frequency. And the clue of its positioning can be used for cognitive radio optimization. |
format | Online Article Text |
id | pubmed-9122215 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | Public Library of Science |
record_format | MEDLINE/PubMed |
spelling | pubmed-91222152022-05-21 Sensing spectrum sharing based massive MIMO radar for drone tracking and interception Junior, Milembolo Miantezila Guo, Bin PLoS One Research Article Radar sensors are becoming crucial for environmental perception in a world with the tremendous growth of unmanned aerial vehicles (UAVs) or drones. When public safety is a concern, the localization of drones are of great significance. However, a drone used for a wrong motive can cause a serious problem for the environment and public safety, given the fact that the dynamic movement of a drone’s emission signal and location tracking is different from existing positioning. This study proposes a safety zone characterized by the presence of N radars sensors with a goal to track and destabilized rogue drones attending to penetrate safety zones (stadium and school). Specifically, a new joint estimation based on a Gaussian filter has been introduced for spectrum sharing and detection awareness. The profit of this novel sensing method can be clearly seen when the two joint hidden states are taken into consideration. Therefore, the drone’s emission state is analyzed by estimating its movement jointly. Considering the drone’s unknown states and actual positioning, an algorithm is developed based on dynamic states space model. Where Bernoulli filter model is designed to estimate recursively the unknown stages of the drone and its changing location based on time. Meanwhile a power control acted from the radar to the targeted drones so that rogue drones are optimally tracked and destabilized over time. Furthermore, an expanding mechanism has been generated to accurately track the drone and enhance detection. A thoughtful result of the experimentation shows clearly that, even when the drone is moving, spectral detection can be performed accurately by chasing its positions. Its demonstrates at 90% of credibility that the original signal has a direct effect on the propagated signal. Therefore, the magnitude of the Doppler shift increases with frequency. And the clue of its positioning can be used for cognitive radio optimization. Public Library of Science 2022-05-20 /pmc/articles/PMC9122215/ /pubmed/35594276 http://dx.doi.org/10.1371/journal.pone.0268834 Text en © 2022 Junior, Guo https://creativecommons.org/licenses/by/4.0/This is an open access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. |
spellingShingle | Research Article Junior, Milembolo Miantezila Guo, Bin Sensing spectrum sharing based massive MIMO radar for drone tracking and interception |
title | Sensing spectrum sharing based massive MIMO radar for drone tracking and interception |
title_full | Sensing spectrum sharing based massive MIMO radar for drone tracking and interception |
title_fullStr | Sensing spectrum sharing based massive MIMO radar for drone tracking and interception |
title_full_unstemmed | Sensing spectrum sharing based massive MIMO radar for drone tracking and interception |
title_short | Sensing spectrum sharing based massive MIMO radar for drone tracking and interception |
title_sort | sensing spectrum sharing based massive mimo radar for drone tracking and interception |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9122215/ https://www.ncbi.nlm.nih.gov/pubmed/35594276 http://dx.doi.org/10.1371/journal.pone.0268834 |
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