Omnidirectional Human Activity Recognition With Spatial-Temporal Trajectory-Based Compensation Using Monostatic MIMO Radar

Recent of radar-based human activity recognition (HAR) generally focus on activities radial to the radar. When it comes to omnidirectional ones, the susceptibility of the Doppler effect to aspect angle brings distortions between measured features and true ones. It will result in significant performance degradation. A novel omnidirectional HAR framework fusing spatial-temporal trajectory compensation (STTC) and multichannel long short-term memory (LSTM) based on a monostatic radar system is proposed to address this issue by normalizing omnidirectional features into radial ones. It can realize omnidirectional HAR by only using radial data in model training. First, a time sequence of feature points indicating measured distance, Doppler, and azimuth angle can be obtained from received signals and located in a range-angle-time (RAT) cube by the fast Fourier transform (FFT) and multisignal classification (MUSIC) algorithm. Next, a spatial-temporal trajectory estimation algorithm is designed to achieve an estimated movement trajectory in the cube. Then, a movement trajectory-based compensation method is proposed to suppress displacement and Doppler distortions according to the geometrical relationship between movement and radar main beam direction. Finally, multichannel LSTM models are designed to support the sequential feature points as input for classification. Contrast experiment results demonstrate its feasibility and superiority in classification accuracy and angle insensitivity compared to existing ones.