对于形目标的轴向维度估计的L1-受限分数顺序梯度下降
Yue Dai1, Shiyuan Zhang1, Guoqiang Guo1
1Nanjing Research Institute of Electronic Technology, Nanjing 210039, China.
Sensors (Basel, Switzerland)
|August 28, 2025
概括
这项研究引入了一种使用高范围分辨率配置文件 (HRRP) 和L1规范梯度下降的新型尺寸估计方法. 在复杂的环境中准确确定目标大小,降低异常值的灵敏度并提高识别性能.
科学领域:
- 电磁散射
- 目标识别
- 信号处理
背景情况:
- 高距离分辨率配置文件 (HRRP) 包含用于目标识别的关键结构信息.
- 精确的大小估计对于在复杂的电磁环境中有效识别目标至关重要.
- 传统的方法通常与异常灵敏度和计算效率相扎.
研究的目的:
- 通过HRRP开发一个强大而有效的弹道目标大小估计方法.
- 通过准确地将实际目标尺寸与预测尺寸逆转来提高目标识别性能.
- 在处理噪音数据方面克服传统L2标准方法的局限性.
主要方法:
- 使用几何光学建立了预测和实际目标尺寸之间的分析关系模型.
- 构建了一个L1规范错误函数以实现强大的优化.
- 采用适应式顺序调整分数顺序梯度下降与动态顺序切换,以提高计算效率和准确性.
主要成果:
- 提出的方法在复杂的电磁环境中实现了精确的尺寸反转.
- 与传统的L2标准方法相比,对异常值的敏感性有所降低.
- 即使有0.4米的测量误差,实际尺寸反转误差也保持在3.7%以下.
结论:
- 在基于HRRP的目标尺寸估计中,L1规范变量分数级梯度下降方法提供了显著的进步.
- 该方法为在具有挑战性的条件下识别目标提供了强大且计算效率高的解决方案.
- 这种方法增强了HRRPs结构信息的利用,以提高绩效.
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