A target state estimation method based on multiple quadrotors is proposed for unknown maneuvering targets, and a distributed formation control method Image-Based Visual Servoing (IBVS) is also proposed to achieve encirclement tracking of unknown maneuvering targets. In the tracking control, collision avoidance constraints for nodes within the formation are also introduced, and based on the shared position information within the formation, the positions of other nodes within the Field of View (FOV) of each node are predicted for detecting unknown targets. Firstly, an Interacting Multiple Model (IMM) was designed based on multiple motion modes to estimate the position and velocity of the target. A virtual camera coordinate system containing translational and yaw rotations was established between the formation and the target based on the estimated values. Then, a distributed control method based on IBVS was further designed by combining image deviation. At the same time, a safe distance between nodes within the formation was introduced, and collision avoidance constraints of the Control Barrier Function (CBF) were designed. Finally, the position of the formation nodes within the FOV was predicted. The simulation results demonstrate that, utilizing the proposed estimation method, the estimation accuracy for target velocity improves by 26.5% in terms of Root Mean Square Error (RMSE) compared to existing methods. Furthermore, the proposed control method enables quadrotor formations to successfully achieve encirclement tracking of unknown maneuvering targets, significantly reducing tracking errors in comparison to conventional approaches.

Active area protection against unauthorized UAV swarms requires coordinated target assignment strategies that account for both low-level physical capabilities and the stochastic, consumptive nature of physical interceptions. This paper presents a decentralized target assignment framework based on probabilistic flow optimization. By utilizing an aerodynamics-aware flight model, we derive a probabilistic prior to capture the geometric dependency of terminal interception success under high-velocity maneuvers. Modeling the defense process as a probabilistic consumption flow couples initial tactical assignments with conditional transition flows, allowing surviving defensive assets to be proactively redistributed to secondary unauthorized intrusions. To resolve this problem under practical communication and sensing constraints, we develop the Distributed Flow-regularized Market-based Consensus (DFMC) algorithm. The proposed algorithm decomposes the global optimization into localized subproblems and employs a water-filling projection to plan secondary paths. Simulation results demonstrate that the proposed framework yields improved interception rates and better spatial resource dispersion compared to conventional auction-based baselines, while maintaining stable scalability in dense interception scenarios.