Abstract: To address the issue that the presence of dynamic clutter in actual environments will affect the accuracy of human target localization and vital signs detection, a coarse-to-fine point cloud selection strategy and an adaptive variational modal decomposition method based on the quality factor are proposed to achieve the suppression of dynamic clutter and the enhancement of vital signs detection performance. First, coarse point clouds of the human body and dynamic objects are distinguished by autocorrelation analysis. Second, a spectrum-based multi-feature fusion model is proposed to select fine point cloud with strong vital signs. Third, a quality factor-based variational mode decomposition method is proposed to separate the dynamic clutter and weak vital signals. Finally, a harmonic weighting selection algorithm is proposed to adaptively extract the respiratory and heartbeat components. Experiments conducted in cluttered indoor environments show that the proposed method effectively mitigates the effects of dynamic clutter and achieves accurate detection of human vital signs in dynamic environments, achieving respiratory and heart rate accuracies of 98.01% and 98.14%, respectively.