CAE-MAS: Convolutional Autoencoder Interference Cancellation for Multiperson Activity Sensing With FMCW Microwave Radar

Human activity sensing is a crucial component of health monitoring and smart environment applications. Frequency-modulated continuous-wave (FMCW) radars can be used for target tracking, but their collected data are usually accompanied by a significant amount of interference, especially in indoor env...

Full description

Saved in:
Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement Vol. 73; pp. 1 - 10
Main Authors: Raeis, Hossein, Kazemi, Mohammad, Shirmohammadi, Shervin
Format: Journal Article
Language:English
Published: New York The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2024
Subjects:
Activity recognition
Continuous radiation
Data collection
Indoor environments
Interference
Tracking
ISSN:0018-9456, 1557-9662
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:Human activity sensing is a crucial component of health monitoring and smart environment applications. Frequency-modulated continuous-wave (FMCW) radars can be used for target tracking, but their collected data are usually accompanied by a significant amount of interference, especially in indoor environments hosting multiple human subjects, leading to a decrease in accuracy. In this article, we propose a method that compensates that interference and can detect individual activities of multiple humans, overcoming existing methods’ limitation of detecting single human activities. To this end, a range–Doppler map of the data is extracted with an FWCW radar, and the interference effect of this map is mitigated by a convolutional autoencoder (CAE). The CAE network learns to attenuate false-positive regions to strengthen the target areas. This is followed by a Gaussian filter, and then the targets are revealed by applying derivatives on both dimensions of the map. Evaluation results show that our method reaches activity recognition accuracies of 97.13% and 73.37% in the cases of one and two humans, respectively.
Bibliography:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2024.3366575