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To address the challenges of speaker recognition during communication among manned and unmanned aerial vehicle (UAV) formations, considering the limitations of relatively low computational speed and limited storage capacity on the UAV platforms, a scheme is proposed that applies non-linear partitioning (NLP) technology to collaborative communication systems. This scheme employs single Gaussian model multi-pass training and dual-model decision-making methods to train the model. Experimental results demonstrate that the use of NLP for speech segmentation, combined with multi-pass training and dual-model decision-making, can significantly enhance the accuracy of speaker recognition during collaborative communication among multi-vehicle formations, thereby improving task execution efficiency.

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Application and Improvement of Non-linear Segmentation Technology in Collaborative Communication Systems of Manned and Unmanned Aerial Vehicle Formations

  • 会议论文

  • 发表日期：

    2025年

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  • Shicong Lin,
  • Xiaowei Wang,
  • Qin Wei

摘要

To address the challenges of speaker recognition during communication among manned and unmanned aerial vehicle (UAV) formations, considering the limitations of relatively low computational speed and limited storage capacity on the UAV platforms, a scheme is proposed that applies non-linear partitioning (NLP) technology to collaborative communication systems. This scheme employs single Gaussian model multi-pass training and dual-model decision-making methods to train the model. Experimental results demonstrate that the use of NLP for speech segmentation, combined with multi-pass training and dual-model decision-making, can significantly enhance the accuracy of speaker recognition during collaborative communication among multi-vehicle formations, thereby improving task execution efficiency.