Multi-radar interference experiment and performance evaluations on algorithm-based and learning-based schemes
Pages 254 - 259
Abstract
The demand for high-resolution automotive radar operating in the 77GHz band is on the rise, especially as we approach the practical implementation of autonomous driving technology. With the increasing prevalence of in-vehicle Chirp Sequence (CS) radars in the future, there is a growing concern about the potential for broadband inter-radar interference. This interference poses a significant risk, potentially leading to a higher likelihood of undetected targets. To address this problem, various algorithm-based and learning-based schemes have been proposed to suppress the inter-radar interference, e.g., iterative threshold based zero suppression method and RNN (Recurrent Neural Network) based interference suppression method. However, they only demonstrate their effectiveness based on simulation results. In this paper, we conducted a multi-radar interference experiment with up to four interference sources and compared the performance of different schemes by using the collected real data.
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Index Terms
- Multi-radar interference experiment and performance evaluations on algorithm-based and learning-based schemes
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Published In
December 2023
1058 pages
ISBN:9798400708916
DOI:10.1145/3628797
Copyright © 2023 Owner/Author.
This work is licensed under a Creative Commons Attribution International 4.0 License.
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Association for Computing Machinery
New York, NY, United States
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Published: 07 December 2023
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SOICT 2023
SOICT 2023: The 12th International Symposium on Information and Communication Technology
December 7 - 8, 2023
Ho Chi Minh, Vietnam
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Overall Acceptance Rate 147 of 318 submissions, 46%
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