摘要Addressing challenges such as low performance,high data signal-to-noise ratio requirements,and lim-ited real-time capabilities in existing heart rate detection methods based on millimeter wave radar,this study presents a heart rate sensing approach tailored for weak vital sign signals characterized by low signal-to-noise ratio and missing data.The method applies a signal mask for echo sequences with variable length.Building upon this signal mask,a signal mapping technique that leverages morphology is devised to mitigate interference and noise.Additionally,learnable position encoding is incorporated to capture temporal features within the signal.Subsequently,a transformer encoder module is employed for matching and computation,culminating in the de-velopment of a time-series global regression model based on deep learning framework.Following the preparation of the dataset and model training,the proposed approach is validated by performance analysis experiments,inter-ference resistance tests,and comparative experiments.Results indicate that this method achieves an impressive accuracy of 96.30％within signal durations ranging from 2 s to 5 s,and it is suitable for scenarios involving miss-ing data and noise interference.Importantly,this approach effectively enables a precise heart rate sensing from short-duration radar signals.