A Deep Learning based approach on Radar Interference Mitigation for Autonomous Vehicles

Todawat, Hritwik; Kakkar, Deepti; Kaur, Gurjot

doi:10.1109/gcat55367.2022.9972166

Cited by 3 publications

(2 citation statements)

References 17 publications

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“…In recent years, deep learning based methods have also been proposed and demonstrated excellent performance based on simulation results [16][17][18][19][20][21][22]. A Convolutional Neural Network (CNN)-based approach for interference mitigation on inter-radar interference was investigated in [16].…”

Section: Introductionmentioning

confidence: 99%

“…In [18], a Fully Convolutional Network (FCN) was proposed to mitigate the interference and noise in the time-frequency spectrum obtained by the Short-Time Fourier Transform (STFT) algorithm. Instead of coping with interference directly, deep learning was also employed for the classification and detection purpose in [19]. In [20], a two-dimensional CNN working on covariance matrices of signals extracted from the region of interest as well as the information of chirp positions was proposed.…”

Section: Introductionmentioning

confidence: 99%

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Experimental Evaluations on Learning-Based Inter-Radar Wideband Interference Mitigation Method

KOIZUMI,

WANG,

UMEHIRA

et al. 2024

IEICE Trans. Fundamentals

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In recent years, high-resolution 77GHz band automotive radar, which is indispensable for autonomous driving, has been extensively investigated. In the future, as vehicle-mounted CS (chirp sequence) radars become more and more popular, intensive inter-radar wideband interference will become a serious problem, which results in undesired miss detection of targets. To address this problem, learning-based wideband interference mitigation method has been proposed, and its feasibility has been validated by simulations. In this paper, firstly we evaluated the tradeoff between interference mitigation performance and model training time of the learning-based interference mitigation method in a simulation environment. Secondly, we conducted extensive inter-radar interference experiments by using multiple 77GHz MIMO (Multiple-Input and Multiple-output) CS radars and collected real-world interference data. Finally, we compared the performance of learning-based interference mitigation method with existing algorithm-based methods by real experimental data in terms of SINR (signal to interference plus noise ratio) and MAPE (mean absolute percentage error).

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