A Non-line-of-sight Human Activity Recognition Method Based on Radar Multi-spectrogram

In this paper, we investigate a lightweight human activity recognition (HAR) method for non-line-of-sight (NLOS) scenarios. This approach constructs a lightweight multi-spectrogram parallel feature extraction model to extract time-range (TR) map and time-Doppler (TD) map features from stepped-frequency continuous wave (SFCW) radar, enabling NLOS HAR in L-shaped corner environments. Specifically, the model incorporates a multiscale depthwise separable convolution to extract multipath TR map features at different scales in NLOS scenarios. Simultaneously, it combines depthwise separable convolution with Ghost convolution to extract features from the TD map. Finally, the extracted multispectrogram features are fused and classified, achieving robust HAR in NLOS environments. To validate the effectiveness of the proposed method, we conducted experimental verification using real-world data from eight types of human activities in an L-shaped corner NLOS scenario. The results show that the proposed model achieves a recognition accuracy of 87.12%, which is a 5.82% improvement compared to the state-of-the-art recognition methods.