TY - GEN
T1 - Radar Signatures based Classification under Strict System Limitations
AU - Huber, Christian
AU - Blazek, Thomas
AU - Xu, Chunlei
AU - Gaich, Andreas
AU - Pathuri Bhuvana, Venkata
AU - Feger, Reinhard
PY - 2022/10
Y1 - 2022/10
N2 - Due to the wide availability of 5G mobile networks, joint communication and radar sensing (JCRS) receives increasing attention by research communities. Here, radar sensing can be done as a side product of communication without additional hardware costs. In contrast to dedicated radar systems, the maximum range as well as the range resolution of these systems are limited. In this paper, we have investigated the limitations of radar systems through a classification problem, recognizing 10 digit-shaped foil balloons. For this purpose, we have recorded a dataset using a 77-GHz frequency modulated continuous wave (FMCW) radar. Furthermore, we have created multiple datasets with different quality levels by reducing the range resolution and the snapshot rate of the recorded measurements. Finally, we have analyzed the behaviours of two machine learning (ML) approaches, random forests (RF) and multilayer perceptron (MLP) to understand the limitations of restricted systems.
AB - Due to the wide availability of 5G mobile networks, joint communication and radar sensing (JCRS) receives increasing attention by research communities. Here, radar sensing can be done as a side product of communication without additional hardware costs. In contrast to dedicated radar systems, the maximum range as well as the range resolution of these systems are limited. In this paper, we have investigated the limitations of radar systems through a classification problem, recognizing 10 digit-shaped foil balloons. For this purpose, we have recorded a dataset using a 77-GHz frequency modulated continuous wave (FMCW) radar. Furthermore, we have created multiple datasets with different quality levels by reducing the range resolution and the snapshot rate of the recorded measurements. Finally, we have analyzed the behaviours of two machine learning (ML) approaches, random forests (RF) and multilayer perceptron (MLP) to understand the limitations of restricted systems.
UR - https://www.scopus.com/pages/publications/85150196974
U2 - 10.1109/IEEECONF56349.2022.10051912
DO - 10.1109/IEEECONF56349.2022.10051912
M3 - Conference proceedings
T3 - Conference record Asilomar Conference on Signals, Systems, and Computers
SP - 564
EP - 568
BT - 2022 56th Asilomar Conference on Signals, Systems, and Computers
A2 - Matthews, Michael B.
ER -