Design and Analysis of Cost-Efficient Sensor Deployment for Tracking Small UAS with Agent-Based Modeling.

Area of Science:

• Engineering
• Computer Science
• Security Studies

Background:

• Commercial unmanned aerial systems (UAS) are increasingly prevalent, posing safety risks in public venues like parks and stadiums.
• Effective detection and tracking of small UAS are crucial for mitigating public safety threats.
• Existing sensor systems may not be cost-efficient or optimally deployed for varied public area densities.

Purpose of the Study:

• To design, model, and evaluate a cost-efficient sensor system for detecting and tracking small UAS.
• To identify optimal sensor configurations and deployments that balance detection rates and cost.
• To prioritize sensor coverage in crowded public areas for enhanced safety.

Main Methods:

• Utilized Agent-Based Modeling (ABM) to simulate a system of interacting agents representing sensors and UAS.
• Simulated various sensor types and quantities within a designated area to assess performance.
• Evaluated cost-efficiency and detection capabilities of different sensor deployment strategies.

Main Results:

• Identified optimal sensor configurations and deployment strategies for effective small UAS detection.
• Demonstrated the ability of ABM to analyze sensor systems for cost-efficiency and coverage.
• Generated a list of candidate sensor configurations and deployments for real-world application.

Conclusions:

• The developed sensor system and deployment strategies offer a cost-efficient solution for mitigating UAS threats in public spaces.
• Agent-Based Modeling is a viable technique for optimizing sensor network design in complex environments.
• The findings provide practical guidance for implementing effective UAS detection and tracking systems in real-world scenarios.