An IR-UWB Multi-Sensor Approach for Collision Avoidance in Indoor Environments

This article aims to propose new techniques to detect and distinguish humans from moving machines in indoor environments. Although many research efforts have been already dedicated to humans' indoor detection, most of the work has been focused on counting people and crowd measurement for consum...

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Bibliographic Details
Published in:IEEE transactions on instrumentation and measurement 2022, Vol.71, p.1-13
Main Authors: Khan, Faheem, Azou, Stephane, Youssef, Roua, Morel, Pascal, Radoi, Emanuel, Dobre, Octavia A.
Format: Article
Language:English
Subjects:
Algorithms
Artificial neural networks
Clutter
Collision avoidance
Constant false alarm rate
Data integration
Engineering Sciences
Feature extraction
human detection and localization
impulse radio ultrawideband (UWB)
Indoor environments
Location awareness
monostatic radar array
multipath
Radar
Radar detection
Radar tracking
Sensors
Signal and Image processing
smart sensing
Ultrawideband
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Summary:This article aims to propose new techniques to detect and distinguish humans from moving machines in indoor environments. Although many research efforts have been already dedicated to humans' indoor detection, most of the work has been focused on counting people and crowd measurement for consumer business applications. Our objective is to develop a reliable approach for humans' indoor detection and localization aiming at avoiding collisions inside a mixed Industry 4.0 manned and unmanned environment to enhance personal and equipment safety and to prevent unwanted intrusions. An original aspect of our research is that we have worked on the real-time estimation of humans' and moving machines' positions while addressing the problems of multipath components and noise clutter detection. A multipulse constant false alarm rate detection algorithm is also proposed for removing the misdetections due to heavy clutter components in the indoor environment. Four impulse radio ultrawideband transceivers are placed in a specific geometry and data fusion is performed to reduce the influence of multipath and noise on the detection process. A convolutional neural network (CNN) is then used to extract the patterns corresponding to a moving machine and humans and classify them accordingly. Experiments have been carried out in two different indoor environments to demonstrate the performance of the proposed algorithms.
ISSN:0018-9456
1557-9662
DOI:10.1109/TIM.2022.3150582