基于通过CEEMD-SVD进行信号重建的增强FFT-Root-MUSIC算法,用于FMCW雷达的联合射程和速度估计
Jiaxin Cao1,2, Huiyue Yi1, Wuxiong Zhang1
1Key Laboratory of Science and Technology on Micro-System, Shanghai Institute of Microsystem and Information Technology Chinese Academy of Sciences, Shanghai 200050, China.
Sensors (Basel, Switzerland)
|January 8, 2025
概括
这项研究引入了频率调制连续波 (FMCW) 雷达的新方法,以改善范围和速度估计. CEEMD-SVD-FRM算法在杂的环境中提高了准确性,信号与噪声比 (SNR) 低.
科学领域:
- 雷达系统工程 雷达系统工程
- 信号处理 信号处理
- 数据分析 数据分析
背景情况:
- 在FMCW雷达中传统的联合范围-速度估计在低信号噪声比 (SNR) 条件下遭受性能降低.
- 添加的白色高斯噪声显著影响节拍信号分析的准确性.
研究的目的:
- 提出一种新的算法,用于在FMCW雷达中进行可靠的联合距离-速度估计,在低SNR环境中特别有效.
- 通过提高节拍信号的信号噪声比,提高目标参数提取的准确性和可靠性.
主要方法:
- 使用互补组合实证模式分解 (CEEMD) 来分解噪音节拍信号.
- 将单值值分解 (SVD) 应用于选定的内在模式函数 (IMFs),以实现有效的信号消噪.
- 通过结合IMF和剩余的方法重建消灭的节拍信号.
- 在重建的信号上使用FFT-Root-MUSIC算法进行联合范围和速度估计.
主要成果:
- 拟议的CEEMD-SVD-FRM算法在范围和速度估计的稳定性和准确性方面取得了显著改进.
- 与传统方法相比,有效地消除节拍信号的噪声会带来更高的性能,特别是在低SNR条件下.
- 模拟和实验验证证证实了算法的有效性.
结论:
- CEEMD-SVD-FRM算法为需要精确范围和速度测量的FMCW雷达系统提供了实质性的进步.
- 结合CEEMD和SVD的方法提供了有效的降噪,使得即使在信号较弱的情况下,目标检测和参数估计也可靠.
- 这种方法在具有挑战性的低SNR操作场景中显著提高了FMCW雷达的能力.
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