When detecting respiratory rate and heart rate in an FMCW radar room, there is a lot of static clutter and white Gaussian noise generated by hardware heat loss in the environment, which makes the separation of respiratory and heartbeat signals poor. At the same time, the harmonic component of the respiratory signal in the frequency domain will affect the estimation of heart rate. To solve the above problems, a spatial blind source estimation method was proposed to accurately estimate respiratory heart rate. Firstly, the weighted principal component analysis (WPCA) algorithm was used to extract the features of the target signal from the IF signal, and then the respiratory heart rate signal was reconstructed according to the different features. Then, the multi-signal classification (MUSIC) algorithm is used to convert the respiration and heartbeat signals into the zero domain to avoid the influence of the respective harmonic components on the detection results. The experimental results showed that the accuracy of respiratory rate detection and heart rate detection was 94.51% and 97.79%, respectively. Compared with the traditional algorithm, the proposed method is stable and has higher detection accuracy.