Paper link: https://doi.org/10.1049/rsn2.12468
Abstract: Millimetre-wave radar has been widely used in health monitoring and human activity recognition owing to its improved range resolution and operation in a variety of environmental conditions. With the MIMO antenna array, 4D radar is increasingly employed in autonomous driving, while its application in assisted living is recent and therefore the value added compared to the increase in signal processing and hardware requirements is still an open question. A model for 4D Time-division multiplexing (TDM) multiple-input-multiple-output (MIMO) frequency-modulated Continuous wave radar is established using the human activities from the HDM05 motion capture dataset. The simulator produces an end-to-end simulation, including four human motions (jumping Jack, kick, punch, and walk), signal time of flight, noise, MIMO signal processing, and classification. Different pre-processing and point cloud-based methods are compared to obtain an average classification accuracy of 90% with PointNet. This study simulates a specific 4D TDM MIMO radar configuration to benchmark signal pre-processing algorithms, which can also assist other researchers to generate range-Doppler-time (range-Doppler time) point cloud data sets for human activities testing different radar configurations, array configurations, and activities saving valuable time in human resources and hardware development before prototyping to assess expected performances.