Ray Tracing Assisted Radar Detection in 6G
Moilanen, Ilkka; Lintonen, Timo; Kiviranta, Markku; Sangi, Pekka; Pyhtilä, Juha; Pirinen, Pekka; Juntti, Markku (2023-12-11)
Moilanen, Ilkka
Lintonen, Timo
Kiviranta, Markku
Sangi, Pekka
Pyhtilä, Juha
Pirinen, Pekka
Juntti, Markku
IEEE
11.12.2023
I. Moilanen et al., "Ray Tracing Assisted Radar Detection in 6G," 2023 IEEE 98th Vehicular Technology Conference (VTC2023-Fall), Hong Kong, Hong Kong, 2023, pp. 1-6, doi: 10.1109/VTC2023-Fall60731.2023.10333844.
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© 2023 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:oulu-202401231422
https://urn.fi/URN:NBN:fi:oulu-202401231422
Tiivistelmä
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.
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.
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