Pedestrian Recognition with Radar Data-Enhanced Deep Learning Approach Based on Micro-Doppler Signatures
This work addresses pedestrian recognition for radar-based systems, but it is incremental as it builds on existing deep learning methods with data enhancement and multi-characteristic learning.
The paper tackles the problem of pedestrian recognition using radar micro-Doppler signatures by addressing data scarcity with a data-enhanced multi-characteristic learning model, achieving accuracy improvements of 3.33% to 10.24% over other studies and a run time of 0.9324 seconds on a 25-minute dataset.
As a hot topic in recent years, the ability of pedestrians identification based on radar micro-Doppler signatures is limited by the lack of adequate training data. In this paper, we propose a data-enhanced multi-characteristic learning (DEMCL) model with data enhancement (DE) module and multi-characteristic learning (MCL) module to learn more complementary pedestrian micro-Doppler (m-D) signatures. In DE module, a range-Doppler generative adversarial network (RDGAN) is proposed to enhance free walking datasets, and MCL module with multi-scale convolution neural network (MCNN) and radial basis function neural network (RBFNN) is trained to learn m-D signatures extracted from enhanced datasets. Experimental results show that our model is 3.33% to 10.24% more accurate than other studies and has a short run time of 0.9324 seconds on a 25-minute walking dataset.