关键词: 无人机对抗, 兰彻斯特方程, OODA环, 防空体系, 集群作战

Abstract:

The classical Lanchester equations， formulated as a set of ordinary differential equations， describe the continuous mutual attrition of opposing forces in intense confrontation over time under deterministic or stochastic conditions， thereby enabling dynamic assessment of combat effectiveness. To enhance the performance of the observe-orient-decide-act （OODA） loop in anti-UAV air defense systems， and to address the insufficient decision-making speed and strike efficiency under UAV swarm saturation attacks， this paper proposes an OODA loop decision-making method based on an improved Lanchester equation. The proposed approach incorporates an information factor， a damage efficiency factor， and a fire allocation weight factor into the traditional Lanchester equation to construct a multi-arm， multi-target confrontation modeling framework. Real-time battle damage assessment results are utilized to dynamically adjust the target allocation weights， which achieves optimal allocation of fire resources. Simulation experiments using a representative anti-UAV operational scenario demonstrate that the method significantly increases the damage rate of red forces against blue UAV swarms， reduces force attrition， and improves combat effectiveness.

Key words: UAV confrontation, Lanchester equation, OODA loop, air defense system, swarm operations