分布式防御杀伤网密度的定义与评估方法应用

Definition and application of evaluation method for kill web density in distributed defense

摘要:

为提高复杂对抗环境下分布式防御体系的资源调度效率与战场适应性,解决传统表征方法以局部能力为主、难以全局精细化量化资源时空分布特性的问题,将空间密度评估引入分布式防御效能分析,提出一种基于杀伤网密度的分布式防御能力表征新方法。首先,提出了杀伤网密度的概念,以量化资源时空分布特性,在此基础上结合威胁加权与网格化空间分割,提出一种基于杀伤网密度的分布式防御能力表征新方法。其次,通过系统定义杀伤网密度的理论模型,结合指控中心、跟踪雷达、制导雷达,以及发射车的多维度约束条件,构建了基于极坐标系分区的动态密度评估框架,量化各单元内有效杀伤链的时空分布特性,并提出了分区密度相对极差指标,实现对资源部署均衡性的精细化评估。最后,通过典型攻防场景仿真验证分析方法有效性。结果表明,所提方法能够有效区分威胁驱动型资源倾斜与被动部署存在强弱点,可为分布式防御资源优化部署与临机调度提供评估基础,提升分布式防御在实战中适应性和高效性。

Abstract:

To improve the resource scheduling efficiency and battlefield adaptability of distributed defense systems in complex adversarial environments and to solve the problem that traditional characterization methods mainly focus on local capabilities and are difficult to finely quantify the global spatiotemporal distribution characteristics of resources, this paper introduced spatial density evaluation into the effectiveness analysis of distributed defense and proposed a new characterization method for distributed defense capability based on kill web density. First, this paper proposed the concept of kill web density to quantify the spatiotemporal distribution characteristics of resources. On this basis, combined with threat weighting and gridded spatial partitioning, this paper proposed a new characterization method for distributed defense capability based on kill web density. Secondly, by systematically defining the theoretical model of kill web density and combining the multi-dimensional constraints of command and control centers, tracking radars, guidance radars, and launch vehicles, this paper constructed a dynamic density evaluation framework based on polar coordinate system partitioning to quantify the spatiotemporal distribution characteristics of effective kill chains within each unit and proposed a relative range index of partition density to achieve the refined evaluation of resource deployment balance. Finally, the effectiveness of the analysis method was verified through the simulation of typical attack-defense scenarios. The results indicate that the proposed method can effectively distinguish between threat-driven resource bias and the strengths and weaknesses existing in passive deployment, can provide an evaluation basis for the optimal deployment and adaptive scheduling of distributed defense resources, and enhances the adaptability and high efficiency of distributed defense in actual combat.