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To address the challenges of high-tempo, complex red-blue confrontations, we utilize large-sample, ultra-real-time simulation software to project potential future confrontation scenarios based on current conditions. Using the simulation process and results, we propose a situation in a data analysis method that extracts and encodes two sides data into a high-dimensional coding matrix. This is followed by a dimensionality reduction approach that projects the high-dimensional data into a two-dimensional space. Finally, a clustering-based ranking method is applied to assess and rank future confrontation scenarios.

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Mission Confrontation Future Reachable Situation Ranking by Large-Sample High-Dimensional Confrontation Data

  • 会议论文

  • 发表日期：

    2026年

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  • Xiaowen Guo,
  • Yuehong Chen,
  • Ti Zhou,
  • Linghao Li,
  • Rong Mu,
  • Qi Zhang

摘要

To address the challenges of high-tempo, complex red-blue confrontations, we utilize large-sample, ultra-real-time simulation software to project potential future confrontation scenarios based on current conditions. Using the simulation process and results, we propose a situation in a data analysis method that extracts and encodes two sides data into a high-dimensional coding matrix. This is followed by a dimensionality reduction approach that projects the high-dimensional data into a two-dimensional space. Finally, a clustering-based ranking method is applied to assess and rank future confrontation scenarios.