强大的集体分类器用于在各种操作条件下先进的合成光圈雷达目标分类
Noor Rahman1, Muzammil Khan2, Imran Khan2
1Department of Computer Science, Virtual University, Islamabad, VIBD01, Pakistan.
Scientific reports
|April 1, 2025
概括
本研究引入了一个改进的组合分类框架,用于合成孔径雷达 (SAR) 自动目标识别 (ATR). 增强系统使用剩余神经网络 (ResNet),在各种操作条件下实现更高的准确性和稳定性.
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
- 机器学习 机器学习
背景情况:
- 合成开口雷达 (SAR) 自动目标识别 (ATR) 在各种操作条件下面临挑战.
- 现有的方法,包括卷积神经网络 (CNN),在概括和一致性方面存在局限性.
研究的目的:
- 为SAR ATR开发一个增强的整体分类框架.
- 提高在标准运行条件 (SOC) 和扩展运行条件 (EOC) 下的稳定性和分类准确性.
主要方法:
- 剩余神经网络 (ResNet),支持矢量机器 (SVM) 和模板匹配的整合.
- 使用多数投票来结合个别分类器的优势.
- 用ResNet取代AlexNet,以获得更优质的特征提取和分类.
主要成果:
- 基于ResNet的组合在SOC下达到了92.67%的准确率,在EOC下达到了88.9%.
- 超越了基于CNN的合奏 (90.30%的SOC,87.22%的EOC).
- 与单个方法相比,在分类准确性和稳定性方面取得了显著的改进.
结论:
- 拟议的ResNet集成组合框架提供了增强的SAR ATR性能.
- 这种方法有效地解决了诸如噪音,屏蔽和视角变化等挑战.
- 该框架显示了对现实世界SAR ATR挑战的实际适用性.
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