TY - GEN
T1 - Floor Map Reconstruction Through Radio Sensing and Learning By a Large Intelligent Surface
AU - Vaca-Rubio, Cristian J.
AU - Pereira, Roberto
AU - Mestre, Xavier
AU - Gregoratti, David
AU - Tan, Zheng-Hua
AU - Carvalho, Elisabeth de
AU - Popovski, Petar
PY - 2022/6/21
Y1 - 2022/6/21
N2 - Environmental scene reconstruction is of great interest for autonomous robotic applications, since an accurate representation of the environment is necessary to ensure safe interaction with robots. Equally important, it is also vital to ensure reliable communication between the robot and its controller. Large Intelligent Surface (LIS) is a technology that has been extensively studied due to its communication capabilities. Moreover, due to the number of antenna elements, these surfaces arise as a powerful solution to radio sensing. This paper presents a novel method to translate radio environmental maps obtained at the LIS to floor plans of the indoor environment built of scatterers spread along its area. The usage of a Least Squares (LS) based method, U-Net (UN) and conditional Generative Adversarial Networks (cGANs) were leveraged to perform this task. We show that the floor plan can be correctly reconstructed using both local and global measurements.
AB - Environmental scene reconstruction is of great interest for autonomous robotic applications, since an accurate representation of the environment is necessary to ensure safe interaction with robots. Equally important, it is also vital to ensure reliable communication between the robot and its controller. Large Intelligent Surface (LIS) is a technology that has been extensively studied due to its communication capabilities. Moreover, due to the number of antenna elements, these surfaces arise as a powerful solution to radio sensing. This paper presents a novel method to translate radio environmental maps obtained at the LIS to floor plans of the indoor environment built of scatterers spread along its area. The usage of a Least Squares (LS) based method, U-Net (UN) and conditional Generative Adversarial Networks (cGANs) were leveraged to perform this task. We show that the floor plan can be correctly reconstructed using both local and global measurements.
KW - cs.CV
KW - eess.SP
KW - Machine Learning for Communication
KW - LIS
KW - Sensing
KW - Computational Imaging
UR - http://www.scopus.com/inward/record.url?scp=85142676230&partnerID=8YFLogxK
U2 - 10.1109/MLSP55214.2022.9943430
DO - 10.1109/MLSP55214.2022.9943430
M3 - Article in proceeding
T3 - Machine Learning for Signal Processing
BT - 2022 IEEE 32nd International Workshop on Machine Learning for Signal Processing, MLSP 2022
PB - IEEE (Institute of Electrical and Electronics Engineers)
T2 - 2022 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING
Y2 - 22 August 2022 through 25 November 2022
ER -