A Paradigm Shift From an Experimental-Based to a Simulation-Based Framework Using Motion-Capture Driven MIMO Radar Data Synthesis

The development of radar-based classifiers driven by empirical data can be highly demanding and expensive due to the unavailability of radar data. In this article, we introduce an innovative simulation-based approach that addresses the data scarcity problem, particularly for our multiple-input multiple-output (MIMO) radar-based direction-independent human activity recognition (HAR) system. To simulate realistic MIMO radar signatures, we first synthesize human motion and generate corresponding spatial trajectories. From these trajectories, a received radio frequency (RF) signal is synthesized using our MIMO channel model, which considers the non-stationary behavior of human motion and the multipath components originating from the scatterers on human body segments. Subsequently, the synthesized RF signals are processed to simulate MIMO radar signatures for various human activities. The proposed simulation-based direction-independent HAR system achieves a classification accuracy of 97.83% when tested with real MIMO radar data. A significant advantage of our simulation-based framework lies in its ability to facilitate multistage data augmentation techniques at the motion-layer, physical-layer, and signal-layer syntheses. This capability significantly reduces the training workload for radar-based classifiers. Importantly, our simulation-based proof-of-concept is applicable to single-input single-output (SISO) and MIMO radars in monostatic, bistatic, and multistatic configurations, making it a versatile solution for realizing other radar-based classifiers, such as gesture classifiers.