# A Multi-Characteristic Learning Method with Micro-Doppler Signatures for Pedestrian Identification

arXiv: https://arxiv.org/abs/2203.12236 · HTML: https://mlsanity.com/paper/2203.12236/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 26 references that are in the map.
- First posted on arXiv 23 Mar 2022.

## Builds on (most cited first, 4 of 4)

- [An Overview of Multi-Task Learning in Deep Neural Networks](https://mlsanity.com/paper/1706.05098/) (2017)
- [Fast Neural Network Predictions from Constrained Aerodynamics Datasets](https://mlsanity.com/paper/1902.00091/) (2019)
- [Aerodynamic Data Predictions Based on Multi-task Learning](https://mlsanity.com/paper/2010.09475/) (2020)
- [Person Identification and Body Mass Index: A Deep Learning-Based Study on Micro-Dopplers](https://mlsanity.com/paper/1811.07173/) (2018)

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

- [Pedestrian Recognition with Radar Data-Enhanced Deep Learning Approach Based on Micro-Doppler Signatures](https://mlsanity.com/paper/2306.08303/) (2023)

## Abstract (arXiv)

The identification of pedestrians using radar micro-Doppler signatures has become a hot topic in recent years. In this paper, we propose a multi-characteristic learning (MCL) model with clusters to jointly learn discrepant pedestrian micro-Doppler signatures and fuse the knowledge learned from each cluster into final decisions. Time-Doppler spectrogram (TDS) and signal statistical features extracted from FMCW radar, as two categories of micro-Doppler signatures, are used in MCL to learn the micro-motion information inside pedestrians' free walking patterns. The experimental results show that our model achieves a higher accuracy rate and is more stable for pedestrian identification than other studies, which make our model more practical.