An Enhanced Radar Sensing Algorithm With Time-Frequency Spectrum Augmentation and Activity Recognition

Radar, with its privacy-preserving features and robust stability, shows immense promise in the field of wireless sensing. In previous research, the time-frequency analysis (TFA) methods are predominantly applied to preprocessed signals to produce spectrum for the activity recognition. Nevertheless, the limited resolution of spectrums, coupled with the constraints imposed by the Heisenberg uncertainty principle, results in frequency errors that can impact recognition accuracy. Therefore, we propose a network architecture for radar sensing called UHAR-Net, which consists of: Augmented Spectrum Network (AS-Net), Multi-Spectrum Fusion module, and Multispectral Behavior Identification Network (MBI-Net). Proposed AS-Net can enhance the time-frequency resolution (TFR) of the spectrum and suppresses the frequency errors. Multi-Spectrum Fusion module combines enhanced spectrums of different resolutions as input for the MBI-Net, which uses a complex-valued neural network (CVNN) for feature extraction and obtaining the final activity results. Moreover, proposed Frequency-Axis Phase Modulation module is used to improve the global feature extraction capability of the convolutional neural network (CNN). Subsequently, a series of experiments including human activity recognition (HAR) tasks, gait recognition tasks, and simulation signal experiments, ablation experiments, and parameter comparison experiments are performed. These experiments result suggest that the proposed UHAR-Net can be applied to a wider range of radar sensing fields.