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用于SAR自动目标识别与数据增强的权重剩余网络.

Junyu Li1, Cheng Peng1

  • 1School of Electrical and Mechanical Engineering, Hefei Technology College, Hefei, China.

Frontiers in neurorobotics
|January 3, 2024
PubMed
概括

本研究引入了一种新的数据增强策略和加权的ResNet模型,以改进合成光圈雷达 (SAR) 自动目标识别 (ATR). 该方法提高了培训效率和识别准确性,克服了常见的SAR ATR挑战.

科学领域:

  • 雷达系统工程 雷达系统工程
  • 人工智能的人工智能
  • 图像处理 图像处理

背景情况:

  • 合成开口雷达 (SAR) 自动目标识别 (ATR) 面临着持续的挑战,包括噪音易感性,大数据集要求和漫长的训练时间.
  • 深度学习已经推进了SAR ATR,但现有的局限性阻碍了广泛应用和最佳性能.
  • 克服噪音和数据稀缺性对于强大的SAR ATR系统至关重要.

研究的目的:

  • 为SAR图像开发一种新的数据增强技术,以解决噪声和数据限制的问题.
  • 引入一个高效的网络架构,加权ResNet,以提高SAR ATR性能.
  • 为了减少训练时间,提高SAR自动目标识别的准确性.

主要方法:

  • 一个数据增强策略,涉及控制添加和删除斑点噪声,以扩大训练数据集.
  • 开发一个修改后的ResNet架构,称之为加权ResNet,结合剩余应变控制以提高计算效率.
  • 利用噪声干扰来人工增加SAR ATR模型的训练数据的多样性和范围.

主要成果:

  • 拟议的数据增强方法在与加权ResNet.net相结合时,显著减少了模型训练时间.
  • 实验分析证实使用新方法改善了SAR ATR能力.
关键词:
自动目标识别 (ATR) 系统数据增强数据增强深度学习人工智能合成光圈雷达 (SAR) 是一种合成光圈雷达.权重的剩余网络 权重的剩余网络

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  • 权重的ResNet模型在减少数据要求的情况下表现出更好的性能.

    • 结论:

    • 数据增强和加权ResNet的综合方法为SAR ATR提供了显著的进步.
    • 与现有的SAR ATR技术相比,这种方法实现了更高的计算效率和识别精度.
    • 拟议的战略为增强SAR图像分析和目标识别提供了有价值的解决方案.