A Study on the Intelligent Drone Threat Evaluation Framework based on Bayesian Networks

Key Points

• The central aim is to develop a Bayesian network framework for evaluating the threat intent of drones using uncertain sensor data.
• Proposing a Bayesian network-based framework for threat assessment.
• Structuring threat factors into three hierarchical stages: trajectory movement, target indicators, and environmental factors.
• Defining discrete states, thresholds, and conditional probabilities for input nodes.
• Analyzing the framework's inference characteristics qualitatively across five distinct threat scenarios.
• Demonstrated the framework's ability to evaluate potential drone threats based on multiple uncertain sensor inputs.
• Showed qualitative advantages over existing rule-based systems in complex tactical environments.
• Provided a theoretical guideline for future intelligent anti-drone system decision-making support models.

Abstract