基于改进EEMD的穿墙雷达动目标微多普勒特性分析

穿墙雷达动目标探测中人的心跳、呼吸、手臂摆动等运动的微多普勒信号是非线性、非平稳信号，可以采用经验模式分解(EMD)对其进行时频分析。由于EMD分解存在模式混合问题，该文提出一种改进的整体平均经验模式分解(EEMD)方法，并将其应用于穿墙雷达人的运动微多普勒特性分析中，并且对分解后的每个本征模式函数(IMF)进行Hilbert-Huang变换(HHT)，得到信号的时间-频率-能量谱。仿真数据和实验结果分析均表明，改进的EEMD方法不仅能够有效消除EMD中的模式混合问题，将人运动微多普勒信号中的不同频率尺度分解在不同的IMF中，而且还能够有效抑制原始信号中的噪声，提高信噪比，得到更精细、更清晰的时频分布。

Micro-Doppler Character Analysis of Moving Objects Using Through-Wall Radar Based on Improved EEMD

The micro-Doppler signals of human’s heartbeat, breathe and arm-moving using through-wall radar are nonlinear and non-stationary, which can be analyzed by Empirical Mode Decomposition (EMD). Due to the mode mixing problem in EMD, an improved Ensemble Empirical Mode Decomposition (EEMD) is proposed in this paper, and is applied to the human micro-Doppler character analysis of the through-wall radar. The time-frequency-energy spectrum is obtained by using Hilbert-Huang Transform (HHT) to every Intrinsic Mode Functions (IMF). The analysis on simulation data and experimental results show that the improved EEMD can effectively eliminate the mode mixing problem in EMD, which means different frequency scales in human’s micro-Doppler signals are decomposed in different IMF. Furthermore, this method can restrain the noise in the original signal and more detail information can be seen clearly in the time-frequency spectrum.