Method for UAV air combat situation assessment under incomplete information

doi:10.3785/j.issn.1008-973X.2025.11.022

Journal of ZheJiang University (Engineering Science)

2025, Vol. 59

Issue (11): 2430-2438 DOI: 10.3785/j.issn.1008-973X.2025.11.022

Method for UAV air combat situation assessment under incomplete information

Yu WANG(

),Shuo LI,Zhan ZHANG,Guanglei MENG

School of Automation, Shenyang Aerospace University, Shenyang 110136, China

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Abstract

A multi-mode variable-weight spatiotemporal fusion evidential network method based on an evidence correction mechanism was proposed in order to address the issue that traditional situation assessment methods struggled to continuously provide effective results when information was incomplete due to their fixed models and reasoning mechanisms. Evidence types were classified according to the degree of temporal correlation of information, and a modular evidential network reasoning model was constructed. A multi-mode evidence correction method was proposed combined with temporal information prediction technology based on the type and incompleteness degree of evidence information. An adaptive generation method for network node weights was proposed considering the differences in the reliability of corrected evidence and based on the variable weight principle. The temporal correlation of non-mutant evidence was strengthened, and the accuracy of continuous situation assessment was improved by designing a three-step spatiotemporal fusion mechanism with a time-decreasing discount rate. The effectiveness of the proposed method was verified through UAV air combat simulation experiments under two scales with four levels of information incompleteness.

Key words： situation assessment incomplete information evidence correction weight adjustment spatio-temporal fusion

Received: 23 October 2024 Published: 30 October 2025

CLC:

TP 391

Fund: 国家自然科学基金资助项目（61906125，62373261）；辽宁省高校基本科研业务费资助项目（LJ232410143020，LJ212410143047）.

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	Yu WANG
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	Zhan ZHANG
	Guanglei MENG

Cite this article:

Yu WANG,Shuo LI,Zhan ZHANG,Guanglei MENG. Method for UAV air combat situation assessment under incomplete information. Journal of ZheJiang University (Engineering Science), 2025, 59(11): 2430-2438.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2025.11.022 OR https://www.zjujournals.com/eng/Y2025/V59/I11/2430

面向不完备信息的无人机空战态势评估方法

针对传统态势评估方法因模型和推理机制固定，在信息不完备时难以持续提供有效结果的问题，提出基于证据修正机制的多模式变权时空融合证据网络方法. 按照信息的时间关联程度划分证据类型，构建模块化证据网络推理模型. 基于证据信息的类型和不完备程度，结合时序性信息预测技术，提出多模式证据修正方法. 考虑修正证据可靠度差异同时基于变权原理，提出网络节点权值的自适应生成方法. 通过设计折扣率随时间递减的三步时空融合机制，强化非突变型证据的时间关联，提升连续性态势评估的准确性. 分别通过两规模下4种信息不完备程度的无人机空战仿真实验，验证方法的有效性.

关键词： 态势评估, 不完备信息, 证据修正, 权值调整, 时空融合

Fig.1 Air combat situation assessment flow of MVWSF-EN

Fig.2 Modular model for situation assessment

Tab.1 Description of node variables

Tab.2 Initial fusion rule

Fig.3 Selection process of evidence input and correction mode

Tab.3 Method of evidence amendment

Fig.4 CNN-GRU network model

Fig.5 Inverse conversion process of predicted belief expectation

Fig.6 Variable-weight spatiotemporal fusion process of non-mutant network module

Fig.7 One against one track

Tab.4 One-on-one air combat information

Fig.8 Switching of evidence input mode

Fig.9 Comparison of results of various methods under incomplete information in one against one scenario

Fig.10 Two against one track

Fig.11 Situation assessment result of ablation experiment

Tab.5 Average running time comparison of four methods


[1]	XU X M, YANG R N, FU Y Situation assessment for air combat based on novel semi-supervised naive Bayes[J]. Journal of Systems Engineering and Electronics, 2018, 29 (4): 768- 779 doi: 10.21629/JSEE.2018.04.11

[2]	SUN Y, MA P, DAI J, et al A cloud Bayesian network approach to situation assessment of scouting under water targets with fixed-wing patrol aircraft[J]. CAAI Transactions on Intelligence Technology, 2022, 8 (2): 532- 545

[3]	LI W N, ZHANG W G, LIU B N, et al The situation assessment of UAVs based on an improved whale optimization Bayesian network parameter-learning algorithm[J]. Drones, 2023, 7 (11): 655 doi: 10.3390/drones7110655

[4]	GAO L, JIANG J F, XU J W, et al SAPFIS: a parallel fuzzy inference system for air combat situation assessment[J]. Journal of Supercomputing, 2024, 81 (1): 73

[5]	李山, 权文, 李昉, 等基于改进D-S理论的多时刻空中目标威胁评估[J]. 电光与控制, 2024, 31 (3): 48- 52 LI Shan, QUAN Wen, LI Fang, et al Multi-moment aerial target threat assessment based on improved D-S theory[J]. Electronics Optics and Control, 2024, 31 (3): 48- 52

[6]	WANG H B, GUAN X, YI X Evidence network inference recognition method based on cloud model[J]. Electronics, 2023, 12 (2): 318 doi: 10.3390/electronics12020318

[7]	ZHOU X L, TANG Y C A note on incomplete information modeling in the evidence theory[J]. IEEE Access, 2019, 7: 166410- 166414 doi: 10.1109/ACCESS.2019.2954132

[8]	TANG Y C, ZHOU D Y, CHAN F T S An extension to Deng’s entropy in the open world assumption with an application in sensor data fusion[J]. Sensors, 2018, 18 (6): 1902 doi: 10.3390/s18061902

[9]	CHEN Y T, TANG Y C An improved approach of incomplete information fusion and its application in sensor data-based fault diagnosis[J]. Mathematics, 2021, 9 (11): 1292 doi: 10.3390/math9111292

[10]	郝启凯, 荀盼盼, 鄂超然, 等基于变异系数法和改进TOPSIS法的空战威胁评估[J]. 火炮发射与控制学报, 2024, 45 (5): 56- 63 HAO Qikai, XUN Panpan, E Chaoran, et al Air combat threat assessment based on the coefficient of variation method and improved TOPSIS method[J]. Journal of Gun Launch and Control, 2024, 45 (5): 56- 63

[11]	杨爱武, 李战武, 李宝, 等基于动态变权重的空战态势评估[J]. 兵工学报, 2021, 42 (7): 1553- 1563 YANG Aiwu, LI Zhanwu, LI Bao, et al Air combat situation assessment based on dynamic variable weight[J]. Acta Armamentarii, 2021, 42 (7): 1553- 1563 doi: 10.3969/j.issn.1000-1093.2021.07.023

[12]	华家辉, 孙鑫, 陈晓东, 等基于集群分析的空中作战目标威胁评估技术研究[J]. 战术导弹技术, 2023, 218 (2): 96- 104 HUA Jiahui, SUN Xin, CHEN Xiaodong, et al Research on air objective threat assessment technology based on cluster analysis[J]. Tactical Missile Technology, 2023, 218 (2): 96- 104

[13]	YANG J Y, LI C H, LI S Y, et al Anti-interference recognition for aerial infrared object based on convolutional feature inference network[J]. IEEE Aerospace and Electronic Systems Magazine, 2023, 78 (2): 4- 15

[14]	ZHANG K, WEI J Y, WANG T T, et al Air target recognition algorithm based on mixed depth features in the interference environment[J]. Optik, 2021, 245: 167535 doi: 10.1016/j.ijleo.2021.167535

[15]	ZHANG X Y, HUANG W, LIN X, et al Complex image recognition algorithm based on immune random forest model[J]. Soft Computing, 2020, 24 (16): 12643- 12657 doi: 10.1007/s00500-020-04706-0

[16]	王昱, 任田君, 范子琳, 等基于角度特征的分布式DDPG无人机追击决策[J]. 控制理论与应用, 2025, 42 (7): 1356- 1366 WANG Yu, REN Tianjun, FAN Zilin, et al Distributed DDPG UAV pursuit decision based on angle feature[J]. Control Theory and Applications, 2025, 42 (7): 1356- 1366 doi: 10.7641/CTA.2024.30105

[17]	王昱, 章卫国, 傅莉, 等基于改进证据网络的空战动态态势估计方法[J]. 航空学报, 2015, 36 (12): 3896- 3909 WANG Yu, ZHANG Weiguo, FU Li, et al Dynamic situation assessment method of aerial warfare based on improved evidence network[J]. Acta Aeronautica et Astronautica Sinica, 2015, 36 (12): 3896- 3909

[18]	余蕾, 邹志科, 刘凤丽, 等湖北漳河灌区中稻气象产量变化特征及预测模型[J]. 长江科学院院报, 2024, 41 (2): 82- 90 YU Lei, ZOU Zhike, LIU Fengli, et al Variation features and estimation model for meteorological yield of mid-season rice in Zhanghe irrigated area of Hubei Province[J]. Journal of Changjiang River Scientific Research Institute, 2024, 41 (2): 82- 90 doi: 10.11988/ckyyb.20221141

[19]	贺宝记, 白林亭, 文鹏程. 基于态势评估及DDPG算法的一对一空战格斗控制方法 [J]. 航空工程进展, 2024, 15(2): 179-187. HE Baoji, BAI Linting, WEN Pengcheng. One-on-one air combat control method based on situation assessment and DDPG algorithm [J]. Advances in Aeronautical Science and Engineering, 2024, 15(2): 179-187.

[20]	余付平, 黄益恒, 沈堤, 等 D-S证据理论在空中目标识别中的应用现状与展望[J]. 电光与控制, 2024, 31 (4): 75- 86 YU Fuping, HUANG Yiheng, SHEN Di, et al Application status and prospects of D-S evidence theory in aerial target identification[J]. Electronics Optics and Control, 2024, 31 (4): 75- 86 doi: 10.3969/j.issn.1671-637X.2024.04.012

[21]	张堃, 张振冲, 刘泽坤, 等基于FD-TODIM的混杂空战多目标动态威胁评估[J]. 系统工程与电子技术, 2023, 45 (1): 148- 154 ZHANG Kun, ZHANG Zhenchong, LIU Zekun, et al Multi-target dynamic threat assessment in mixed air combat based on FD-TODIM[J]. Systems Engineering and Electronics, 2023, 45 (1): 148- 154

[22]	王昱, 关智慧, 李远鹏基于轨迹预测和分布式MADDPG的无人机集群追击决策[J]. 计算机应用, 2024, 44 (11): 3623- 3628 WANG Yu, GUAN Zhihui, LI Yuanpeng Distributed UAV cluster pursuit decision-making based on trajectory prediction and MADDPG[J]. Journal of Computer Applications, 2024, 44 (11): 3623- 3628 doi: 10.11772/j.issn.1001-9081.2023101538

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