Comparison of Learning Models for Wideband Interference Mitigation in Automotive Chirp Sequence Radar Systems
Year: 2024
Type: article
Abstract: 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.
Source: IEICE Communications Express
Authors: Yudai Suzuki, Xiaoyan Wang, Masahiro Umehira
Institutions: Ibaraki University, Nanzan University
Language: English
Subfield: Aerospace Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4401453136