基于改进希尔伯特-黄变换的进动目标周期估计

Period Estimation of Precession Targets Based on Improved Hilbert-Huang Transform

针对进动目标的微多普勒周期估计问题,提出了一种基于改进希尔伯特-黄变换的提取算法。该算法通过将希尔伯特-黄变换中的经验模态分解(EMD)替换为完备总体经验模态分解(CEEMDAN),对目标回波信号进行分解得到各本征模态函数(IMF)后,再对IMF进行希尔伯特谱分析,从该希尔伯特谱中提取出目标信号中的微多普勒周期。仿真表明,所采用的方法能有效地克服EMD算法噪声环境中性能较差的缺陷,在低信噪比条件下具备较好的性能。

Aiming at the problem of micro-Doppler period estimation of precession targets,an extraction algorithm based on improved Hilbert-Huang transform is proposed.In this algorithm,the empirical mode decomposition(EMD)in Hilbert-Huang transform is replaced by complete ensemble empirical mode decomposition(CEEMDAN).After the target echo signal is decomposed into each intrinsic mode function(IMF),the Hilbert spectrum of IMF is analyzed,and the micro-Doppler period of the target signal is extracted from the Hilbert spectrum.The simulation results show that the method used in this paper can effectively overcome the poor performance of EMD algorithm in noisy environment,and has better performance under the condition of low signal-to-noise ratio.