As radar hardware platforms become smaller and cheaper

indoor radar-based motion recognition applications have become reality and can be implemented in low-cost devices with simple architectures. The carrier-free ultra-wideband (UWB) radar has extremely high resolution

which can capture the slight movement of the human motion and has a strong anti-jamming capability in indoor complex environments. Human motion recognition based on UWB radar compared to video-based also has the advantage of penetrating furniture

walls and protecting personal privacy. Aiming at the defects that the traditional time-frequency analysis method based on radar realizes the human motion recognition is time-consuming and poor real-time performance

the machine learning method is introduced to classify and recognize different types of human motions. The biggest difficulty in introducing machine learning methods for UWB radar human motion recognition is that there are only a few-shot of available UWB radar measured data samples. Therefore

a human motion feature extraction method based on principal component analysis (PCA) and discrete cosine transform (DCT) is proposed. And the support vector machine (SVM) optimized by the improved grid search algorithm is used for human motion recognition under few-shot samples. Finally

simulations experiments are performed based on measured data through three different schemes. Under the condition that there are only 5 groups of training data samples

the average recognition rate of human motion recognition can reach more than 96%.