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旋转机体回声分离使用循环电源引导软面具从无人机雷达信号

Ji'er Wang¹, Jing Sheng¹, He Tian¹

¹National Key Laboratory of Scattering and Radiation, Beijing 100854, China.

Sensors (Basel, Switzerland)

|February 27, 2026

概括

这项研究引入了一种新的循环动力引导软掩盖 (CPGSM) 框架,以改善旋翼飞机的雷达检测. 该方法有效地将旋转器的信号与机身的杂乱分开,增强微多普勒分析.

科学领域:

雷达信号处理是指处理雷达信号的过程.
无人驾驶飞行器 (UAV) 的特征描述
微多普勒签名分析的分析方法

背景情况:

旋转器诱导的微多普勒信号对于基于雷达的旋翼无人机识别至关重要.
强体在接近零的多普勒回报通常会掩盖这些特征,特别是在悬浮或低速场景中.
像EMD和VMD这样的现有方法在低信号噪声比 (SNR) 条件下与模式混合和泄漏作斗争.

研究的目的:

开发一个强大的框架,以将旋转器的信号与雷达回声中的身体回报分开.
为了提高连续性,减少微多普勒结构的泄漏.
为了提高无人机转子速度估计的稳定性和准确性.

主要方法:

提出了一个循环动力引导软面具 (CPGSM) 框架,利用循环静止周期作为旋转机体分离的先决条件.
实施了CPS引导的软掩盖程序来识别一个DC主导的重叠带.
在重叠带内使用循环功率频谱分析来指导时间频率分配,结合其他地方的确定性分配.

主要成果:

与硬直流隔离,EMD和VMD相比,证明了微多普勒连续性的改进和身体泄漏的减少.
在5-30dB SNR范围内实现了更稳定的性能.
在不同的传感配置中提供一致的转子速度估计.

结论:

在基于雷达的无人机表征中,CPGSM框架通过有效分离旋转机和车身签名,为无人机表征提供了显著的进步.
这种方法提高了微多普勒分析的可靠性,特别是在具有挑战性的低SNR环境中.
CPGSM提供了一种基于物理的方法,用于改进转子速度估计和整体无人机监控.

关键词:

循环功率频谱的周期性功率频谱循环静止性分析微多普勒的微型多普勒.转子体分离的方法这是一种柔软的掩饰.

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