UWB Sensing for UAV and Human Comparative Movement Characterization

Area of Science:

• Robotics and Control Systems
• Sensor Technology
• Signal Processing

Background:

• Unmanned aerial vehicles (UAVs) or drones are increasingly implicated in security incidents.
• Distinguishing drone activity from human presence is crucial for indoor security.
• Drones pose a potential threat for unauthorized indoor/office intrusion.

Purpose of the Study:

• To comparatively characterize the movement patterns of drones and humans in an indoor environment.
• To develop methods for differentiating drone and human movement for security applications.
• To assess the feasibility of using UWB sensors for indoor movement detection.

Main Methods:

• Utilized Ultra-Wideband (UWB) sensors to acquire movement signals.
• Applied advanced signal processing techniques, including wavelet transform.
• Employed the phase diagram concept for movement characterization.

Main Results:

• Successfully generated movement maps differentiating drone and human trajectories.
• Demonstrated the effectiveness of wavelet transform and phase diagrams in movement characterization.
• Provided a comparative analysis of drone versus human movement signatures.

Conclusions:

• The proposed methods can effectively distinguish between drone and human movement indoors.
• UWB sensing combined with advanced signal processing offers a viable solution for drone detection.
• This research contributes to enhanced security protocols against unauthorized drone intrusions.