多个目标CFAR检测性能基于一个智能集群算法在K-分布海杂乱.
Mansoor M Al-Dabaa1, Eugen Laslo2, Ahmed A Emran1
1Department of Electrical Engineering, Faculty of Engineering, Al-Azhar University, Cairo 11651, Egypt.
Sensors (Basel, Switzerland)
|April 26, 2025
概括
这项研究介绍了Lin-DBSCAN-CFAR,这是一种先进的检测方案,可以提高复杂的海上杂乱中恒定虚假报警率 (CFAR) 的性能. 该方法有效过干扰目标,提高海上环境中的雷达检测准确性和效率.
科学领域:
- 雷达信号处理是指处理雷达信号的过程.
- 检测理论的检测理论
- 统计信号处理 统计信号处理
背景情况:
- 保持恒定错误报警率 (CFAR) 对于雷达系统在具有K分布的海上混乱的动态海上环境中运行至关重要.
- 传统的CFAR探测器在多目标场景中面临性能限制,原因是干扰目标的掩盖效应.
- 海洋杂乱和干扰目标可以表现为雷达数据中的异常值,使准确的目标检测变得复杂.
研究的目的:
- 开发一种先进的CFAR检测方案,克服传统方法在多目标和复杂的海上杂乱条件下的局限性.
- 通过有效地隔离干扰目标和海峰,提高雷达检测的稳定性和准确性.
- 为了减少先进的CFAR技术的计算复杂性,同时保持高检测性能.
主要方法:
- 集成基于线性密度的空间集群用于噪声应用 (Lin-DBSCAN) 与恒定虚假报警率 (CFAR) 处理.
- 使用Lin-DBSCAN从Cell Under Test (CUT) 参考窗口中识别和隔离干扰目标和海峰.
- 对拟议的Lin-DBSCAN-CFAR与传统的CFAR方法和DBSCAN-CFAR进行评估的比较模拟.
主要成果:
- 与传统的CFAR技术相比,拟议的Lin-DBSCAN-CFAR方法显示了显著提高的检测准确性和稳定性.
- 林-DBSCAN-CFAR有效过异常信号,在复杂的海上杂乱和多目标环境中提高性能.
- 该方法实现了与计算密集型DBSCAN-CFAR可比的检测性能,但复杂性大大降低.
结论:
- 林-DBSCAN-CFAR为在具有挑战性的海上环境中探测雷达目标提供了更优越,更有效的解决方案.
- 拟议的方案需要较低的信号噪声比 (SNR) 来实现所需的检测概率,这表明灵敏度提高.
- 这种先进的探测方案为需要在不同的海上混乱条件下可靠性能的雷达系统提供了实际的进步.
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