杀伤网误差指标分配方法研究

符小卫; 吴军战; 蒋烁

doi:10.12382/bgxb.2025.0304

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杀伤网误差指标分配方法研究

更新时间：2026-05-06

- 杀伤网误差指标分配方法研究
- Research on Error Allocation Methods for Kill Webs
- 兵工学报 2026年47卷第4期页码：250304
- 作者机构：
  
  西北工业大学电子信息学院，陕西西安 710129
- 作者简介：
  
  通信作者邮箱：fxw@nwpu.edu
- 基金信息：
  
  航空科学基金项目(2020Z023053001)
- DOI：10.12382/bgxb.2025.0304
  中图分类号： TP18
- 收稿：2025-04-22，
  
  网络首发：2026-02-25，
  
  纸质出版：2026-04
- 稿件说明：
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符小卫, 吴军战, 蒋烁. 杀伤网误差指标分配方法研究[J]. 兵工学报, 2026,47(4):250304.

FU Xiaowei, WU Junzhan, JIANG Shuo. Research on Error Allocation Methods for Kill Webs[J]. Acta Armamentarii, 2026, 47(4): 250304.
符小卫, 吴军战, 蒋烁. 杀伤网误差指标分配方法研究[J]. 兵工学报, 2026,47(4):250304. DOI： 10.12382/bgxb.2025.0304.

FU Xiaowei, WU Junzhan, JIANG Shuo. Research on Error Allocation Methods for Kill Webs[J]. Acta Armamentarii, 2026, 47(4): 250304. DOI： 10.12382/bgxb.2025.0304.

摘要

随着信息技术的进步、空战战术的不断发展，杀伤网和空空导弹越发受到重视。以杀伤网中的中远距空空导弹为研究对象，以空空导弹截获目标概率来描述杀伤网命中精度，针对杀伤网中误差指标的分配进行研究具有重要意义。针对杀伤网指标分配问题，提出基于量子计算的非支配排序多种群遗传算法，将杀伤网误差指标分配建模为多目标优化问题，对杀伤网中敏感性较高的误差源进行误差指标分配，给出多个满足约束条件的误差逆向量化分配方案，并与带精英策略的非支配排序遗传算法、传统的试凑法和单目标优化法所得误差指标分配方案进行对比，验证了利用该算法从多目标优化角度能够有效求解误差分配问题。

Abstract

With the advancement of information technology and the continuous development of aerial combat tactics

the kill webs and air-to-air missiles have garnered increasing attention. The allocation of error metrics within kill webs is studied by taking the medium- and long-range air-to-air missiles within kill webs as the research object and the target acquisition probability of air-to-air missiles as a metric for hit accuracy. For the kill web metric allocation

a multi-population genetic algorithm based on quantum computing for non-dominated sorting is proposed. The error metric allocation in kill webs is modeled as a multi-objective optimization problem

and the error metrics are allocated to the highly sensitive error sources within the kill web

which provides the error allocation schemes of reverse quantification that satisfy the constraints. The proposed algorithm is compared with NSGA-Ⅱ

traditional trial-and-error and single-objective optimization algorithms. The results confirm that the proposed algorithm can effectively solve the error allocation problems from the perspective of multi-objective optimization.

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