There is an ever-growing demand for vital signs monitoring for a variety of occasions. Non-contact vital signs monitoring can be achieved by detecting the displacement of the human chest using Doppler radar. This method is non-invasive, environment-independent and suitable for long-term monitoring. However, the real-time detection of cardiopulmonary parameters extraction with radar needs to address the challenges of the limited time duration of the signal for the extraction of cardiopulmonary signals, accuracy of vital signs parameters estimation and signal processing algorithm complexity. Here we show that empirical and variational signal decomposition methods can be performed to extract respiration and heartbeat signals in radar system. Hilbert-Huang transform is applied in conjunction with the signal decomposition methods to display the time-frequency-energy distribution of decomposed signals, thus the instantaneous frequencies and amplitudes of vital signs can be obtained. Besides, online signal decomposition approaches are illustrated to achieve the dynamic estimation of vital signs from radar data stream. The results of our experimental verification demonstrate that Online-VMD has an accuracy of 99.56% and a variance of estimated frequencies of 1.81×10−3 when it is applied in FMCW radar system, providing a reliable, accurate and real-time parameter estimation result in vital signs monitoring.