Order-Based Quantitative Inference for Swarm Drone Estimation in Dynamic Environment

In dynamic adversarial environments, the uncertainty in the number of drones deployed by the attacking side within the defender’s territory complicates the counter-drone operations for the defense. Traditional detection methods yield incomplete information, making it difficult for the defense to infer the number of attacking drones and to adapt to the demands of battlefield uncertainty. With limited battlefield information, accurately estimating the number of attacking drones is crucial for optimizing weapon deployment strategies. To address this issue, this section employs a First-Come, First-Served (FCFS) GI/G/c queuing model to simulate the inference process of drone numbers in a dynamic environment. Furthermore, this paper introduces an order-based estimation method to handle the problem of drone number inference in uncertain environments. This innovative approach is capable of inferring the number of drones based on partially observable data. The study also evaluates the effectiveness of this method in different scenarios for inferring the number of various types of drones. To test the robustness of the method, noise is introduced during the inference process in this paper, and its impact is verified. Simulation results demonstrate that the method can effectively infer the number of attacking drones in a battlefield environment with uncertainty, providing strategic support for counter-drone strategies.