Radar Signatures based Classification under Strict System Limitations

Christian Huber; Thomas Blazek; Chunlei Xu; Andreas Gaich; Venkata Pathuri-Bhuvana; Reinhard Feger

doi:10.1109/IEEECONF56349.2022.10051912

Radar Signatures based Classification under Strict System Limitations

Christian Huber, Thomas Blazek, Chunlei Xu, Andreas Gaich, Venkata Pathuri-Bhuvana, Reinhard Feger

Research output: Conference proceeding/Chapter in Book/Report/ › Conference Paper › peer-review

Abstract

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.

Original language	English
Title of host publication	2022 56th Asilomar Conference on Signals, Systems, and Computers
Pages	564-568
Number of pages	5
DOIs	https://doi.org/10.1109/IEEECONF56349.2022.10051912
Publication status	Published - 2 Nov 2022
Event	2022 56th Asilomar Conference on Signals, Systems, and Computers - Pacific Grove, CA, USA Duration: 31 Oct 2022 → 2 Nov 2022

Conference

Conference	2022 56th Asilomar Conference on Signals, Systems, and Computers
Period	31/10/22 → 2/11/22

Keywords

Radar measurements
Target recognition
Training data
Radar
Bandwidth
Reflection
Classification algorithms

Access to Document

10.1109/IEEECONF56349.2022.10051912

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10051912

Cite this

@inproceedings{2160c4f44ee2499bbbbcdf839ae745eb,

title = "Radar Signatures based Classification under Strict System Limitations",

abstract = "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.",

keywords = "Radar measurements, Target recognition, Training data, Radar, Bandwidth, Reflection, Classification algorithms",

author = "Christian Huber and Thomas Blazek and Chunlei Xu and Andreas Gaich and Venkata Pathuri-Bhuvana and Reinhard Feger",

year = "2022",

month = nov,

day = "2",

doi = "10.1109/IEEECONF56349.2022.10051912",

language = "English",

isbn = "978-1-6654-5907-5",

pages = "564--568",

booktitle = "2022 56th Asilomar Conference on Signals, Systems, and Computers",

note = "2022 56th Asilomar Conference on Signals, Systems, and Computers ; Conference date: 31-10-2022 Through 02-11-2022",

}

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 - 2022/11/2

Y1 - 2022/11/2

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.

KW - Radar measurements

KW - Target recognition

KW - Training data

KW - Radar

KW - Bandwidth

KW - Reflection

KW - Classification algorithms

UR - https://ieeexplore.ieee.org/document/10051912/

U2 - 10.1109/IEEECONF56349.2022.10051912

DO - 10.1109/IEEECONF56349.2022.10051912

M3 - Conference Paper

SN - 978-1-6654-5907-5

SP - 564

EP - 568

BT - 2022 56th Asilomar Conference on Signals, Systems, and Computers

T2 - 2022 56th Asilomar Conference on Signals, Systems, and Computers

Y2 - 31 October 2022 through 2 November 2022

ER -

Radar Signatures based Classification under Strict System Limitations

Abstract

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this