Simulation framework for activity recognition and benchmarking in different radar geometries

Abstract

Radar micro-Doppler signatures have been proposed for human monitoring and activity classification for surveillance and outdoor security, as well as for ambient assisted living in healthcare-related applications. A known issue is the performance reduction when the target is moving tangentially to the line of sight of the radar. Multiple techniques have been proposed to address this, such as multistatic radar and to some extent, interferometric (IF) radar. A simulator is presented to generate synthetic data representative of eight radar systems (monostatic, circular multistatic and in-line multistatic [IM] and IF) to quantify classification performances as a function of aspect angles and deployment geometries. This simulator allows an unbiased performance evaluation of different radar systems. Six human activities are considered with signatures originating from motion-captured data of 14 different subjects. The classification performances are analysed as a function of aspect angles ranging from 0° to 90° per activity and overall. It demonstrates that IF configurations are more robust than IM configurations. However, IM performs better at angles below 55° before IF configurations take over.