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Sensing spectrum sharing based massive MIMO radar for drone tracking and interception.

Milembolo Miantezila Junior1, Bin Guo1

  • 1School of Electronics and Information Engineering, Changchun University of Science and Technology, Changchun, Jilin, 130022, China.

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|May 20, 2022
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Summary
This summary is machine-generated.

This study introduces a radar-based system to track and destabilize rogue drones near sensitive areas. The method uses a Gaussian filter and dynamic state models for accurate drone localization and detection.

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Area of Science:

  • Radar sensing
  • Drone technology
  • Public safety

Background:

  • Unmanned aerial vehicles (UAVs) or drones are increasingly used, raising public safety concerns regarding their localization.
  • Rogue drones pose environmental and safety risks due to their dynamic movement and distinct signal characteristics.
  • Existing positioning systems are insufficient for tracking drones with malicious intent.

Purpose of the Study:

  • To propose a novel radar-based safety zone system for tracking and destabilizing rogue drones.
  • To enhance detection awareness and spectrum sharing capabilities using advanced filtering techniques.
  • To develop a robust algorithm for real-time drone localization and threat mitigation.

Main Methods:

  • A joint estimation approach using a Gaussian filter for spectrum sharing and detection awareness.
  • Development of a dynamic state-space model with a Bernoulli filter for recursive estimation of drone states and location.
  • Implementation of a radar power control mechanism for optimal tracking and destabilization of rogue drones.
  • Introduction of an expanding mechanism for enhanced drone tracking accuracy and detection.

Main Results:

  • Accurate spectral detection and tracking of moving drones demonstrated.
  • Credible evidence (90%) showing the direct effect of the original signal on the propagated signal.
  • Confirmation that Doppler shift magnitude increases with frequency, aiding positioning.
  • Validation of the system's effectiveness in identifying and tracking drones within safety zones.

Conclusions:

  • The proposed radar sensing method enables accurate drone localization and tracking, even for moving targets.
  • The joint estimation and dynamic state-space models provide effective means for detecting and mitigating rogue drone threats.
  • The findings support the use of cognitive radio optimization based on drone positioning clues.