机制-数据协作用于描述海洋杂乱属性和训练样本选择
Wenhao Chen1, Yong Zou1, Zhengzhou Li1
1School of Microelectronics and Communication Engineering, Chongqing University, Chongqing 400044, China.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
这项研究引入了一种新的方法,通过改进海上杂乱的特征和训练样本选择来检测海上雷达目标. 该方法提高了各种海洋环境中的检测性能和可靠性.
科学领域:
- 海上雷达技术的技术
- 信号处理 信号处理
- 环境建模环境建模
背景情况:
- 准确的海上杂乱特征对于海上雷达目标检测至关重要.
- 传统的统计模型与复杂的海洋环境和不平衡的训练数据作斗争.
- 不准确的模型和数据导致由于过度装配或不足而导致检测性能差.
研究的目的:
- 提出一种机制-数据协作方法,用于加强海上雷达目标检测.
- 为了准确地描述动态海上环境中的海洋杂乱变化.
- 提高雷达目标探测器的一般化能力.
主要方法:
- 使用散射系数作为一个代表性特征.
- 逐步将经典模型与测量数据相匹配,并将结果合并以弥补不连续性.
- 实施混合特征选择策略,整合全球密度分布和培训样本选择的局部梯度变化.
主要成果:
- 拟议的方法可以在各种场景中准确地描述海洋杂乱的特性.
- 与传统方法相比,可以实现更准确的海洋杂乱特征表示.
- 用拟议样品训练的探测器在各种海上环境中表现出强大的概括能力.
结论:
- 精确的海洋杂乱模型和最佳的训练样本选择对于改善雷达目标检测至关重要.
- 开发的方法提高了基于雷达的海上监视的可靠性.
- 这项工作为海上雷达目标检测方面的挑战提供了强有力的解决方案.
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