Open Access

ARTICLE

Through-Wall Multihuman Activity Recognition Based on MIMO Radar

Changlong Wang¹, Jiawei Jiang¹, Chong Han^1,2,*, Hengyi Ren³, Lijuan Sun^1,2, Jian Guo^1,2

1 College of Computer, Nanjing University of Posts and Telecommunications, Nanjing, 210003, China
2 Jiangsu High Technology Research Key Laboratory for Wireless Sensor Networks, Nanjing University of Posts and Telecommunications, Nanjing, 210023, China
3 College of Information Science and Technology, Nanjing Forestry University, Nanjing, 210000, China

* Corresponding Author: Chong Han. Email:

Computers, Materials & Continua 2025, 83(3), 4537-4550. https://doi.org/10.32604/cmc.2025.063295

Received 10 January 2025; Accepted 27 February 2025; Issue published 19 May 2025

Abstract

Existing through-wall human activity recognition methods often rely on Doppler information or reflective signal characteristics of the human body. However, static individuals, lacking prominent motion features, do not generate Doppler information. Moreover, radar signals experience significant attenuation due to absorption and scattering effects as they penetrate walls, limiting recognition performance. To address these challenges, this study proposes a novel through-wall human activity recognition method based on MIMO radar. Utilizing a MIMO radar operating at 1–2 GHz, we capture activity data of individuals through walls and process it into range-angle maps to represent activity features. To tackle the issue of minimal variation in reflection areas caused by static individuals, a multi-scale activity feature extraction module is designed, capable of extracting effective features from radar signals across multiple scales. Simultaneously, a temporal attention mechanism is employed to extract keyframe information from sequential signals, focusing on critical moments of activity. Furthermore, this study introduces an activity recognition network based on a Deformable Transformer, which efficiently extracts both global and local features from radar signals, delivering precise human posture and activity sequences. In experimental scenarios involving 24 cm-thick brick walls, the proposed method achieves an impressive 97.1% accuracy in activity recognition classification.

---

Keywords

---