Title
Multipath Exploitation for Human Activity Recognition using a Radar Network
Author
Guendel, R.G. (TU Delft Microwave Sensing, Signals & Systems)
Kruse, N.C. (TU Delft Microwave Sensing, Signals & Systems)
Fioranelli, F. (TU Delft Microwave Sensing, Signals & Systems) 
Yarovoy, Alexander (TU Delft Microwave Sensing, Signals & Systems)
Date
2024
Abstract
In this study, the problem of multipath in radar sensor networks for human activity recognition (HAR) has been examined. Traditionally considered as a source of additional clutter, the multipath is being investigated for its potential to be exploited through the creation of virtual radar nodes. These virtual nodes are conceptualized to observe targets from aspect angles that differ from those of physically existing radars. To realize this idea, an innovative processing pipeline is proposed that extracts information from multipath signals to improve HAR. The pipeline isolates and tracks the line-of-sight (LOS) and multipath components of a moving human target performing continuous sequences of activities observed by a network of three radar sensors. Furthermore, the method has been verified with experimental data consisting of six activities and 14 volunteers by comparing classification metrics with the use of a single radar as well as only the LOS components of the three radars in the network. A 12-layer convolutional neural network (CNN) classifier has been designed to operate on range-Doppler (RD) images derived from the LOS and multipath components, extracted by the proposed method. A substantial performance improvement using the leave-one-person-out (L1Po) test set is demonstrated in the order of +11% by exploiting a multiradar network with its LOS and multipath components.
Subject
radar signal processing
radar multipath
multipath
human activity recognition
distributed radar
hierarchical clustering
clustering
multilateration
trilateration
To reference this document use:
http://resolver.tudelft.nl/uuid:afdc3565-30a8-4a0a-8bd6-5e67627b05d4
DOI
https://doi.org/10.1109/TGRS.2024.3363631
Embargo date
2024-08-16
ISSN
1558-0644
Source
IEEE Transactions on Geoscience and Remote Sensing, 62
Bibliographical note
Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.
Part of collection
Institutional Repository
Document type
journal article
Rights
© 2024 R.G. Guendel, N.C. Kruse, F. Fioranelli, Alexander Yarovoy