# Multi-Person Continuous Tracking and Identification from mm-Wave micro-Doppler Signatures

arXiv: https://arxiv.org/abs/2003.03571 · HTML: https://mlsanity.com/paper/2003.03571/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Mar 2020.

## Built on by (most cited first, 8 of 8)

- [RadarScenes: A Real-World Radar Point Cloud Data Set for Automotive Applications](https://mlsanity.com/paper/2104.02493/) (2021)
- [SHARP: Environment and Person Independent Activity Recognition with Commodity IEEE 802.11 Access Points](https://mlsanity.com/paper/2103.09924/) (2021)
- [Cross Vision-RF Gait Re-identification with Low-cost RGB-D Cameras and mmWave Radars](https://mlsanity.com/paper/2207.07896/) (2022)
- [A Review of Indoor Millimeter Wave Device-based Localization and Device-free Sensing Technologies and Applications](https://mlsanity.com/paper/2112.05593/) (2021)
- [A MIMO Radar-based Few-Shot Learning Approach for Human-ID](https://mlsanity.com/paper/2110.08595/) (2021)
- [Attention-Refined Unrolling for Sparse Sequential micro-Doppler Reconstruction](https://mlsanity.com/paper/2306.14233/) (2023)
- [Interpersonal Distance Tracking with mmWave Radar and IMUs](https://mlsanity.com/paper/2303.12798/) (2023)
- [Open-Set Gait Recognition from Sparse mmWave Radar Point Clouds](https://mlsanity.com/paper/2503.07435/) (2025)

## Abstract (arXiv)

In this work, we investigate the use of backscattered mm-wave radio signals for the joint tracking and recognition of identities of humans as they move within indoor environments. We build a system that effectively works with multiple persons concurrently sharing and freely moving within the same indoor space. This leads to a complicated setting, which requires one to deal with the randomness and complexity of the resulting (composite) backscattered signal. The proposed system combines several processing steps: at first, the signal is filtered to remove artifacts, reflections and random noise that do not originate from humans. Hence, a density-based classification algorithm is executed to separate the Doppler signatures of different users. The final blocks are trajectory tracking and user identification, respectively based on Kalman filters and deep neural networks. Our results demonstrate that the integration of the last-mentioned processing stages is critical towards achieving robustness and accuracy in multi-user settings. Our technique is tested both on a single-target public dataset, for which it outperforms state-of-the-art methods, and on our own measurements, obtained with a 77 GHz radar on multiple subjects simultaneously moving in two different indoor environments. The system works in an online fashion, permitting the continuous identification of multiple subjects with accuracies up to 98%, e.g., with four subjects sharing the same physical space, and with a small accuracy reduction when tested with unseen data from a challenging real-life scenario that was not part of the model learning phase.