Work

A Probabilistic Flow Framework for Decentralized Cooperative Active Area Defense in Swarm-on-Swarm Interceptions

Year: 2026

Type: article

Abstract: 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.

Source: Drones

DOI: 10.3390/drones10070540

Authors: Tong Jiang, Xuechen Gu, Jianchuan Ye, Zengzhen Mi, Tao Jiang

Institutions: Chongqing University, Chongqing University of Technology, Beijing Institute of Technology

Language: English

Topic: Military Defense Systems Analysis

Subfield: Aerospace Engineering

Field: Engineering

Domain: Physical Sciences

Open Access status: gold

Estimated APC paid: $3,137

Funder: China Postdoctoral Science Foundation