计算机科学

计算机科学 ›› 2026, Vol. 53 ›› Issue (9): 136-144.doi: 10.11896/jsjkx.260600032

• 数据库&大数据&数据科学 • 上一篇    下一篇

基于分布式缺失数据规则构建的舰船识别方法

胡意晖1, 李妍1, 郭天翔1, 李文涛2, 胡星辰1,3   

  1. 1 国防科技大学系统工程学院 长沙 410073
    2 西南大学人工智能学院 重庆 400715
    3 国防科技大学计算机学院 长沙 410073
  • 收稿日期:2026-06-03 修回日期:2026-08-07 出版日期:2026-09-15 发布日期:2026-09-10
  • 通讯作者: 胡星辰(xingchenhu@nudt.edu.cn)
  • 作者简介:(546271718@qq.com)
  • 基金资助:
    国家自然科学基金(62376279)

Ship Recognition Methods via Rule Construction from Distributed Incomplete Data

HU Yihui1, LI Yan1, GUO Tianxiang1, LI Wentao2, HU Xingchen1,3   

  1. 1 College of Systems Engineering,National University of Defense Technology,Changsha 410073,China
    2 College of Artificial Intelligence,Southwest University,Chongqing 400715,China
    3 College of Computer Science and Technology,National University of Defense Technology,Changsha 410073,China
  • Received:2026-06-03 Revised:2026-08-07 Published:2026-09-15 Online:2026-09-10
  • About author:HU Yihui,born in 1995,master.Her main research interests include data mining and image classification.
    HU Xingchen,born in 1989,Ph.D,associate professor.His main research in-terests include computational intelligence,data mining and information systems.
  • Supported by:
    National Natural Science Foundation of China(62376279).

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摘要： 针对复杂海战场环境下多元探测信息汇聚难、缺失严重,以及现有深度多模态融合模型可解释性不足的问题,提出一种基于分布式缺失数据规则构建的舰船识别方法(Distributed Information-granule and Rule Construction,DIRC)。该方法以信息粒为跨节点共享的结构化载体,在不泄露原始数据的前提下,通过联邦共识约束完成局部缺失视图的语义对齐。在此基础上,构建面向舰船识别任务的TS模糊规则分类器,实现分布式场景下的可解释协同识别;同时,设计自适应加权聚合机制,有效缓解数据异质性导致的聚合偏差问题。基于“灵弈”兵棋推演系统构建的MyFleet数据集的实验表明,所提方法在高缺失场景下仍保持较好的识别性能,在MyFleet数据集75%缺失率下的准确率达到70.42%,且能够输出具有明确物理含义和战术指向的识别规则。所提方法可为受限通信和信息碎片化条件下的舰船目标识别与辅助决策提供一种兼具鲁棒性与可解释性的技术途径。

关键词: 舰船识别, 联邦学习, 分布式缺失数据, TS模糊规则, 可解释性

Abstract: To address the challenges of difficult aggregation of multi-source sensing information,severe observation missingness,and insufficient interpretability of existing deep multimodal fusion models in complex naval battlefield environments,this paper proposes a ship recognition method based on distributed information-granule and rule construction(DIRC).The proposed method employs information granules as structured carriers shared across distributed nodes and achieves semantic alignment of locally incomplete views through federated consensus constraints without exposing raw data.On this basis,a Takagi-Sugeno(TS) fuzzy rule classifier is constructed for ship recognition,enabling interpretable collaborative recognition under distributed conditions.In addition,an adaptive weighted aggregation mechanism is designed to alleviate aggregation bias caused by data heterogeneity.Experimental results on the MyFleet dataset generated from the “Lingyi” wargame simulation system demonstrate that the proposed method maintains favorable recognition performance under high missing-rate conditions,achieving an accuracy of 70.42% at a missing rate of 75%,while also producing recognition rules with explicit physical meanings and tactical implications.The proposed method provides an interpretable and robust technical solution for ship target recognition and decision support under constrained communication and fragmented information conditions.

Key words: Ship recognition, Federated learning, Distributed incomplete data, Takagi-Sugeno fuzzy rules, Interpretability

中图分类号: 

  • TP391
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