TY - GEN
T1 - Detection of wheelchairs in indoor and public spaces using an LTE-based passive radar
AU - Stuart, Reid
AU - Paine, Stephen
AU - Mishra, Amit Kumar
N1 - Publisher Copyright:
© 2024 IEEE.
PY - 2024
Y1 - 2024
N2 - This study explores the potential of leveraging Long Term Evolution (LTE) as an illuminator of opportunity, combined with Machine Learning (ML), to detect the presence of objects in the nearby environment. Specifically, the research focuses on detecting wheelchairs. The machine learning algorithm is trained on datasets generated from the Master Information Block (MIB) and Signal Information Block 1 (SIB1). The data was collected using a software-defined radio (SDR) tuned to LTE band 3. The experimental results showed that an LTE-based passive SDR system can detect the presence of a wheelchair as well as other large objects within a room with a high degree of accuracy.
AB - This study explores the potential of leveraging Long Term Evolution (LTE) as an illuminator of opportunity, combined with Machine Learning (ML), to detect the presence of objects in the nearby environment. Specifically, the research focuses on detecting wheelchairs. The machine learning algorithm is trained on datasets generated from the Master Information Block (MIB) and Signal Information Block 1 (SIB1). The data was collected using a software-defined radio (SDR) tuned to LTE band 3. The experimental results showed that an LTE-based passive SDR system can detect the presence of a wheelchair as well as other large objects within a room with a high degree of accuracy.
UR - https://www.scopus.com/pages/publications/105005752514
U2 - 10.1109/RADAR58436.2024.10993927
DO - 10.1109/RADAR58436.2024.10993927
M3 - Conference Proceeding (ISBN)
AN - SCOPUS:105005752514
T3 - Proceedings of the IEEE Radar Conference
BT - International Radar Conference
PB - Institute of Electrical and Electronics Engineers
T2 - 2024 International Radar Conference, RADAR 2024
Y2 - 21 October 2024 through 25 October 2024
ER -