The decomposition of multi-component micro-Doppler signals overlapping in the time-frequency (T-F) domain is critical and challenging, especially in the case of irregular instantaneous Doppler frequency. The authors propose a novel time-based decomposition method called the short-time variational mode decomposition (STVMD) to analyse the irregular (FM) micro-Doppler signals, and present an optimal model combined with T-F transformation. Then, considering the STVMD may fail to extract the instantaneous frequency (IF) of overlapped components, an improved STVMD algorithm is put forward. Since the dependence of the STVMD algorithm on the initial value, they adopt the Kalman filtering to implement IF tracking and regrouping under the global constraint, further accelerating the convergence of the algorithm. Furthermore, due to the mode aliasing at the intersection point, they adopt a degenerate STVMD model to decompose the signals with known centre frequencies, which can be viewed as a Wiener filter. With the two steps, the improved STVMD algorithm can effectively solve the decomposition of T-F overlapping irregular FM micro-Doppler signals. Compared with the peak ridge technique and the ridge path regrouping and intrinsic chirp component decomposition (RPRG + ICCD), the proposed method shows the effectiveness and adaptability even for irregular FM signals with large T-F spectrum amplitude fluctuation in the low signal-to-noise ratio environment.

中文翻译：

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基于时间的多分量不规则FM微多普勒信号通过STVMD分解

在时频（TF）域中重叠的多分量微多普勒信号的分解至关重要且具有挑战性，尤其是在不规则的瞬时多普勒频率情况下。作者提出了一种新颖的基于时间的分解方法，称为短时变分模式分解（STVMD），以分析不规则（FM）微多普勒信号，并提出了与TF变换相结合的最优模型。然后，考虑到STVMD可能无法提取重叠分量的瞬时频率（IF），提出了一种改进的STVMD算法。由于STVMD算法依赖于初始值，因此他们采用Kalman滤波在全局约束下实现IF跟踪和重新分组，从而进一步加快了算法的收敛速度。此外，由于交叉点处的模式混叠，他们采用简并的STVMD模型分解具有已知中心频率的信号，可以将其视为维纳滤波器。通过这两个步骤，改进的STVMD算法可以有效解决TF重叠不规则FM微多普勒信号的分解。与峰值脊技术，脊路径重组和固有chi成分分解（RPRG + ICCD）相比，该方法即使在低信噪比下具有较大TF频谱幅度波动的不规则FM信号中也显示出了有效性和适应性。环境。改进的STVMD算法可以有效地解决TF重叠不规则FM微多普勒信号的分解问题。与峰值脊技术，脊路径重组和固有chi成分分解（RPRG + ICCD）相比，该方法即使在低信噪比下具有较大TF频谱幅度波动的不规则FM信号中也显示出了有效性和适应性。环境。改进的STVMD算法可以有效地解决TF重叠不规则FM微多普勒信号的分解问题。与峰值脊技术，脊路径重组和固有chi成分分解（RPRG + ICCD）相比，该方法即使在低信噪比下具有较大TF频谱幅度波动的不规则FM信号中也显示出了有效性和适应性。环境。