New Approach of UAV Movement Detection and Characterization Using Advanced Signal Processing Methods Based on UWB...

Area of Science:

• Electrical Engineering
• Signal Processing
• Sensor Technology

Background:

• The commercial unmanned aerial vehicles (UAVs) market is rapidly expanding due to advanced technology, affordability, and user-friendliness.
• The increasing prevalence of drones poses security challenges in sensitive areas, highlighted by a rise in incidents.
• Existing detection methods may not adequately address the complexities of drone movement characterization.

Purpose of the Study:

• To develop and evaluate a novel approach for detecting and characterizing drone movement using ultra-wide band (UWB) sensing.
• To assess the effectiveness of advanced signal processing techniques, including recurrence plot analysis, for drone detection.
• To provide a foundation for enhanced drone intrusion detection systems.

Main Methods:

• Utilizing an ultra-wide band (UWB) sensing system for drone monitoring.
• Applying classical signal processing techniques: correlation, envelope detection, and time-scale analysis.
• Implementing a novel recurrence plot analysis for movement characterization.

Main Results:

• The proposed UWB sensing approach effectively characterizes drone movement.
• Recurrence plot analysis demonstrates potential for accurate drone movement mapping.
• Comparison of methods highlights trade-offs in accuracy and computational time.

Conclusions:

• The UWB sensing system combined with advanced signal processing offers a promising solution for drone detection.
• Recurrence plot analysis provides valuable insights into drone movement patterns.
• This research serves as a basis for developing more robust drone intrusion detection systems.