Review and Simulation of Counter-UAS Sensors for Unmanned Traffic Management

Area of Science:

• Aerospace Engineering
• Electrical Engineering
• Computer Science

Background:

• Safe integration of Unmanned Aerial Systems (UAS) into airspace requires robust noncollaborative surveillance.
• Existing Unmanned Traffic Management (UTM) ecosystems necessitate advanced Counter-UAS (C-UAS) sensor technologies for real-time tracking.

Purpose of the Study:

• To review the state-of-the-art in C-UAS sensor technologies, encompassing academic and commercial developments.
• To propose simulation models for various C-UAS sensors, focusing on key performance parameters and operational processes.
• To evaluate the impact of sensor performance on UTM systems, particularly tracking and tactical conflict resolution.

Main Methods:

• Comprehensive review of current C-UAS sensor technologies (radar, RF, image, acoustic).
• Development of simulation models incorporating probabilistic detection (detection probability, false alarms) and measurement errors.
• Integration of sensor models into a UTM simulation platform to analyze performance under different configurations (monostatic, multistatic, passive).

Main Results:

• Simulation models were developed for active radars, passive radars, and acoustic sensors.
• The models successfully integrated into a UTM simulation platform, allowing for performance evaluation.
• Analysis focused on how sensor characteristics influence tracking accuracy and the identification of tactical conflicts involving drones.

Conclusions:

• The proposed simulation models provide a valuable tool for assessing C-UAS sensor effectiveness within UTM environments.
• Understanding sensor performance is critical for mitigating risks associated with uncontrolled drone operations.
• This research contributes to the development of safer and more efficient airspace management for UAS.