Accurate monitoring of animal vital signs is crucial for health management and disease diagnosis. However, detecting these signals poses several challenges. Breathing and heartbeat signals in animals are weak, with heartbeats easily interfered with by breathing harmonics and noise. Additionally, animal physiology differs from humans, and detection environments can be complex. To address these issues, this study explores millimeter-wave radar-based methods for monitoring vital signs. It proposes an improved adaptive unscented Kalman filter combined with wavelet-based spectral estimation. The approach optimizes the adaptive unscented Kalman filter using a noise weighting factor, maintaining its sensitivity to new observations. It also uses different wavelet bases to extract purer signal features based on the distinct characteristics of heart and breathing rates, employing spectral density estimation for calculating these parameters. The algorithm was validated on 29 cattle and 10 dog datasets, showing accurate measurement. The root mean square errors were 0.030 4 and 0.031 5 for breathing frequency, and 0.057 4 and 0.056 9 for heart rate. Compared to traditional peak - detection algorithms, detection accuracy improved by 3.33% and 7.26% for cattle, and 3.65% and 6.96% for dogs. The algorithm offers high accuracy and strong noise resistance, making it valuable for both theoretical and practical vital-sign detection.