Pets have been indispensable members for many families in modern life, especially significant for the elderly and the blind. However, they may cause false alarm when misused as signal source in non-contact monitoring of the vital signs using ultra-wideband (UWB) radar. Distinguishing between humans and pets can help ensure the correct signal source. Nevertheless, existing solutions are few or only utilize a single feature, which can hinder robustness and accuracy because of individual differences. In this study, we proposed a robust multi-feature based method to solve the problem. First, 19 discriminative features were extracted to reflect differences in aspects of energy, frequency, wavelet entropy, and correlation coefficient. Second, the features were ranked by recursive feature elimination algorithm and the top eight were then selected to build an optimal support vector machine (SVM) model. The area under the receiver operating characteristic curve (AUC) of the optimal SVM model reached 0.9620. The false and missing alarms for identifying humans were 0.0962 and 0.0600, respectively. Finally, comparison with the state-of-the-art method that only employed one feature validated the advance and accuracy of the proposed method. The method is envisioned to facilitate the UWB radar applications in non-contact and continuous vital signs monitoring.

宠物已经成为现代生活中许多家庭不可或缺的成员，对老人和盲人尤为重要。然而，当它们被误用作使用超宽带（UWB）雷达进行生命体征非接触监测的信号源时，它们可能会导致误报。区分人类和宠物有助于确保正确的信号源。然而，现有的解决方案很少或仅使用单个特征，这会由于个体差异而阻碍鲁棒性和准确性。在这项研究中，我们提出了一种鲁棒的基于多特征的方法来解决这个问题。首先提取了19个判别特征，以反映能量、频率、小波熵和相关系数等方面的差异。第二，通过递归特征消除算法对特征进行排序，然后选择前八名来构建最佳支持向量机（SVM）模型。最优SVM模型的受试者工作特征曲线下面积（AUC）达到0.9620。识别人类的误报和漏报分别为 0.0962 和 0.0600。最后，与仅采用一个特征的最先进方法进行比较，验证了所提出方法的先进性和准确性。该方法旨在促进 UWB 雷达在非接触式和连续生命体征监测中的应用。分别。最后，与仅采用一个特征的最先进方法进行比较，验证了所提出方法的先进性和准确性。该方法旨在促进 UWB 雷达在非接触式和连续生命体征监测中的应用。分别。最后，与仅采用一个特征的最先进方法进行比较，验证了所提出方法的先进性和准确性。该方法旨在促进 UWB 雷达在非接触式和连续生命体征监测中的应用。