Distributed Extended Kalman Filtering Based Techniques for 3-D UAV Jamming Localization.

Area of Science:

• Wireless Communication Security
• Network Defense
• Robotics in Security

Background:

• Wireless networks face significant threats from jamming attacks, particularly those utilizing Unmanned Aerial Vehicles (UAVs).
• Jamming, a form of Denial of Service (DoS) attack, intentionally disrupts communication by increasing noise levels at receiver nodes.
• The increasing accessibility of UAVs makes them a potent tool for executing jamming attacks in wireless networks.

Purpose of the Study:

• To propose and evaluate a three-dimensional (3-D) UAV jamming localization scheme.
• To develop distributed algorithms for real-time tracking and detection of UAV jammer positions.
• To compare the efficacy of proposed methods against conventional centralized approaches.

Main Methods:

• Development of two distributed Extended Kalman Filter (EKF) based schemes: Distributed EKF (DEKF) and Distance Ratio aided Distributed EKF (DEKF-DR).
• DEKF utilizes received power data from a single boundary node.
• DEKF-DR incorporates data from an edge node alongside a boundary node for enhanced localization.
• Extensive simulations were performed to assess performance in a 3-D environment.

Main Results:

• The proposed distributed algorithms demonstrated superior performance compared to the conventional Centralized EKF (EKF-Centr) method.
• Both DEKF and DEKF-DR schemes achieved accurate 3-D localization of the UAV jammer.
• The distributed approaches exhibited significantly lower computational complexity than the centralized method.

Conclusions:

• The developed distributed EKF-based schemes are highly effective for 3-D UAV jamming localization.
• These methods provide a more efficient and less complex solution for detecting and tracking jamming threats in wireless networks.
• The findings highlight the potential of distributed algorithms in enhancing wireless communication security against UAV-based attacks.