Ray Tracing Assisted Radar Detection in 6G

Ilkka Moilanen; Timo Lintonen; Markku Kiviranta; Pekka Sangi; Juha  Pyhtilä; Pekka Pirinen; Markku Juntti

doi:10.1109/VTC2023-Fall60731.2023.10333844

Ray Tracing Assisted Radar Detection in 6G

Ilkka Moilanen, Timo Lintonen, Markku Kiviranta, Pekka Sangi, Juha Pyhtilä, Pekka Pirinen, Markku Juntti

BA6409 Future radio networks

Centre for Wireless Communications (CWC)

Research output: Chapter in Book/Report/Conference proceeding › Conference article in proceedings › Scientific › peer-review

2 Citations (Scopus)

Abstract

In this paper, we present a novel method that detects appearing targets and improves their detection probability in a known static environment. Ray tracing-based channel model is used to calculate the radio signal paths using a 3D model of the environment. This preliminary information and the measured radar image are delivered to the convolutional neural network (CNN) based autoencoder (AE). The output image provides an improved representation of appearing targets, as the redundancy coming from known static environment is modeled and cancelled from the radar image. The method does not need the traditional constant false alarm rate (CFAR) threshold setting, which is an advantage, especially in heterogenous environments. The functionality of the method is tested with radar measurements in a laboratory corridor. Our results show that environmental clutter decreases significantly in the radar image and new targets are more clearly distinguished.

Original language	English
Title of host publication	2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)
Publisher	IEEE Institute of Electrical and Electronic Engineers
Number of pages	6
ISBN (Electronic)	979-8-3503-2928-5
ISBN (Print)	979-8-3503-2929-2
DOIs	https://doi.org/10.1109/VTC2023-Fall60731.2023.10333844
Publication status	Published - 11 Dec 2023
MoE publication type	A4 Article in a conference publication
Event	98th IEEE Vehicular Technology Conference, VTC 2023-Fall - Hong Kong, Hong Kong, China Duration: 10 Oct 2023 → 13 Oct 2023

Conference

Conference	98th IEEE Vehicular Technology Conference, VTC 2023-Fall
Country/Territory	China
City	Hong Kong
Period	10/10/23 → 13/10/23

Funding

This work has been supported in part by the Business Finland under the projects AI4Green and 6GLearn, with the corresponding diary numbers 1052/31/2019, 1267/31/2019 and 8603/31/2022, 8738/31/2022. The work of University of Oulu has also been supported in part by the Academy of Finland, 6G Flagship program under Grant 346208.

Keywords

radar
ray tracing
autoencoder
JCAS
6G

Access to Document

10.1109/VTC2023-Fall60731.2023.10333844Licence: Other

Cite this

@inproceedings{e4b3a0f34dd64cdfbe931a72af111e19,

title = "Ray Tracing Assisted Radar Detection in 6G",

abstract = "In this paper, we present a novel method that detects appearing targets and improves their detection probability in a known static environment. Ray tracing-based channel model is used to calculate the radio signal paths using a 3D model of the environment. This preliminary information and the measured radar image are delivered to the convolutional neural network (CNN) based autoencoder (AE). The output image provides an improved representation of appearing targets, as the redundancy coming from known static environment is modeled and cancelled from the radar image. The method does not need the traditional constant false alarm rate (CFAR) threshold setting, which is an advantage, especially in heterogenous environments. The functionality of the method is tested with radar measurements in a laboratory corridor. Our results show that environmental clutter decreases significantly in the radar image and new targets are more clearly distinguished.",

keywords = "radar, ray tracing, autoencoder, JCAS, 6G",

author = "Ilkka Moilanen and Timo Lintonen and Markku Kiviranta and Pekka Sangi and Juha Pyhtil{\"a} and Pekka Pirinen and Markku Juntti",

year = "2023",

month = dec,

day = "11",

doi = "10.1109/VTC2023-Fall60731.2023.10333844",

language = "English",

isbn = "979-8-3503-2929-2",

booktitle = "2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)",

publisher = "IEEE Institute of Electrical and Electronic Engineers",

address = "United States",

note = "98th IEEE Vehicular Technology Conference, VTC 2023-Fall ; Conference date: 10-10-2023 Through 13-10-2023",

}

Moilanen, I , Lintonen, T , Kiviranta, M, Sangi, P, Pyhtilä, J, Pirinen, P & Juntti, M 2023, Ray Tracing Assisted Radar Detection in 6G. in 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall). IEEE Institute of Electrical and Electronic Engineers, 98th IEEE Vehicular Technology Conference, VTC 2023-Fall, Hong Kong, China, 10/10/23. https://doi.org/10.1109/VTC2023-Fall60731.2023.10333844

TY - GEN

T1 - Ray Tracing Assisted Radar Detection in 6G

AU - Moilanen, Ilkka

AU - Lintonen, Timo

AU - Kiviranta, Markku

AU - Sangi, Pekka

AU - Pyhtilä, Juha

AU - Pirinen, Pekka

AU - Juntti, Markku

PY - 2023/12/11

Y1 - 2023/12/11

N2 - In this paper, we present a novel method that detects appearing targets and improves their detection probability in a known static environment. Ray tracing-based channel model is used to calculate the radio signal paths using a 3D model of the environment. This preliminary information and the measured radar image are delivered to the convolutional neural network (CNN) based autoencoder (AE). The output image provides an improved representation of appearing targets, as the redundancy coming from known static environment is modeled and cancelled from the radar image. The method does not need the traditional constant false alarm rate (CFAR) threshold setting, which is an advantage, especially in heterogenous environments. The functionality of the method is tested with radar measurements in a laboratory corridor. Our results show that environmental clutter decreases significantly in the radar image and new targets are more clearly distinguished.

AB - In this paper, we present a novel method that detects appearing targets and improves their detection probability in a known static environment. Ray tracing-based channel model is used to calculate the radio signal paths using a 3D model of the environment. This preliminary information and the measured radar image are delivered to the convolutional neural network (CNN) based autoencoder (AE). The output image provides an improved representation of appearing targets, as the redundancy coming from known static environment is modeled and cancelled from the radar image. The method does not need the traditional constant false alarm rate (CFAR) threshold setting, which is an advantage, especially in heterogenous environments. The functionality of the method is tested with radar measurements in a laboratory corridor. Our results show that environmental clutter decreases significantly in the radar image and new targets are more clearly distinguished.

KW - radar

KW - ray tracing

KW - autoencoder

KW - JCAS

KW - 6G

UR - http://www.scopus.com/inward/record.url?scp=85181169471&partnerID=8YFLogxK

U2 - 10.1109/VTC2023-Fall60731.2023.10333844

DO - 10.1109/VTC2023-Fall60731.2023.10333844

M3 - Conference article in proceedings

SN - 979-8-3503-2929-2

BT - 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall)

PB - IEEE Institute of Electrical and Electronic Engineers

T2 - 98th IEEE Vehicular Technology Conference, VTC 2023-Fall

Y2 - 10 October 2023 through 13 October 2023

ER -

Ray Tracing Assisted Radar Detection in 6G

Abstract

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

6GLearn: Radio Channel Aware Machine Learning based 6G Design

Cite this

Ray Tracing Assisted Radar Detection in 6G

Abstract

Conference

Funding

Keywords

Access to Document

Other files and links

Fingerprint

Projects

6GLearn: Radio Channel Aware Machine Learning based 6G Design

Cite this