# Pedestrian Recognition with Radar Data-Enhanced Deep Learning Approach Based on Micro-Doppler Signatures

arXiv: https://arxiv.org/abs/2306.08303 · HTML: https://mlsanity.com/paper/2306.08303/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 30 references that are in the map.
- First posted on arXiv 14 Jun 2023.

## Builds on (most cited first, 6 of 6)

- [Generative Adversarial Networks: An Overview](https://mlsanity.com/paper/1710.07035/) (2017)
- [Multi-Scale Convolutional Neural Networks for Time Series Classification](https://mlsanity.com/paper/1603.06995/) (2016)
- [Flow Field Reconstructions with GANs based on Radial Basis Functions](https://mlsanity.com/paper/2009.02285/) (2020)
- [Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers](https://mlsanity.com/paper/1811.07173/) (2018)
- [A MIMO Radar-based Few-Shot Learning Approach for Human-ID](https://mlsanity.com/paper/2110.08595/) (2021)
- [A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification](https://mlsanity.com/paper/2203.12236/) (2022)

## Built on by (most cited first, 1 of 1)

- [Vulnerable Road User Detection and Safety Enhancement: A Comprehensive Survey](https://mlsanity.com/paper/2405.19202/) (2024)

## Abstract (arXiv)

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.