RadarSpecAugment: A Simple Data Augmentation Method for Radar-Based Human Activity Recognition

Abstract

In this letter, a simple data augmentation method for micro-Doppler radar-based human activity recognition (HAR) is proposed. The proposed augmentation method can improve the performance of a neural network with insufficient training samples. It is applied directly to the spectrograms of the human activity radar data. The augmentation strategy consists of three operations: 1) time shift, 2) frequency disturbance, and 3) frequency shift. Without destroying this kinematic information in the spectrograms, the three operations are used to change the three attributes, i.e., dynamic-static state, instantaneous speed, and overall speed, of human motion spectrograms. The experimental results show that the proposed augmentation method can significantly improve the recognition accuracy of different classic deep models used in radar-based HAR. Moreover, we performed another experiment that utilizes the different groups of volunteers’ data for training and testing. The results reveal that the generalization ability of the network can be significantly improved by the proposed augmentation method.