汽车啁啾序列雷达系统宽带干扰抑制学习模型的比较

IF 0.3 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC IEICE Communications Express Pub Date : 2024-08-09 DOI:10.23919/comex.2024COL0013

Yudai Suzuki;Xiaoyan Wang;Masahiro Umehira

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引用次数: 0

摘要

CS （Chirp Sequence）雷达对自动驾驶的安全性起着至关重要的作用。然而，它的广泛采用增加了宽带雷达间干扰的可能性，导致目标的漏检。为了解决这个问题，我们利用RNN（循环神经网络）和自注意模型来减轻汽车雷达系统中的宽带干扰，并在信噪比和处理时间方面比较了12种不同的学习模型。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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Comparison of Learning Models for Wideband Interference Mitigation in Automotive Chirp Sequence Radar Systems

CS (Chirp Sequence) radar plays a crucial role in the safety of autonomous driving. However, its widespread adoption increases the probability of wideband inter-radar interference, leading to miss-detection of targets. To address this problem, we utilize RNN (Recurrent Neural Network) and self-attention models to mitigate wideband interference in automotive radar systems and compare 12 different learning models in terms of SNR (Signal-to-Noise Ratio) and processing time.

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来源期刊

IEICE Communications Express ENGINEERING, ELECTRICAL & ELECTRONIC-

自引率

33.30%

发文量

114