Simulation framework for activity recognition and benchmarking in different radar geometries

Boyu Zhou,Francesco Fioranelli,Yier Lin,Julien Le Kernec,Zhiqin Zhao,Olivier Romain,Shufan Yang

doi:10.1049/rsn2.12049

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.

Highlights

Radar signatures, in particular, micro‐Doppler signatures, have attracted significant interests for classification of human activities, both in the outdoor environments for security and surveillance, and in the indoor environments for healthcare and assisted living applications [1,2].An issue for classification based on mD signatures is the performance reduction for targets’ trajectories tangential to the radar line of sight, as the mD frequency shifts are reduced, and it is challenging to extract informative features from the data
Tahmoush [3] showed that mD classification performance dropped to 40% at high aspect angles, and references [4,5,6,7] analysed the classification performance and limitations due to the aspect angle
When the target is not walking in the radial direction, depending on the aspect angle, the salient features for classification may change, and the accuracy of classification reduces as the target velocity gets closer to the tangential direction

Summary

Introduction

An issue for classification based on mD signatures is the performance reduction for targets’ trajectories tangential to the radar line of sight, as the mD frequency shifts are reduced, and it is challenging to extract informative features from the data. In [8], a monostatic radar is used to classify human activities at 0°, 45° and 90° yielding 96%, 97% and 91% accuracy, respectively, using convolutional neural networks (CNN). This was increased to 98% when the directions are fused, but this would require four separate radar systems to acquire data sequentially to avoid interference as opposed to using multiple views simultaneously with multistatic radar

Methods

Results

Conclusion