Prototype Development and Experimental Performance Evaluation of FMCW Radar Using Iterative Interference Suppression Technique

Okuda, Takashi; Makino, Yuya; Umehira, Masahiro; Wang, Xiaoyan; Takeda, Shigeki; Kuroda, Hiroshi

doi:10.1109/radar41533.2019.171335

Cited by 5 publications

(2 citation statements)

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“…Achieving autonomous driving hinges significantly on the effectiveness of onboard sensing techniques in perceiving the surrounding environment. In contrast to camera and LiDAR (Light Detection and Ranging), radar system offers cost-effectiveness and excels in resilience against adverse weather conditions, backlighting, and various other environmental factors [3]. Chirp Sequence (CS) radar, in particular, is considered a promising mainstay in onboard radar systems due to its ability to simultaneously detect distances and relative velocities of multiple targets [4].…”

Section: Introductionmentioning

confidence: 99%

“…Inter-radar interference [7] can be divided into two types: wideband interference [3], which results in an increased noise level in the frequency spectrum and causes the miss detection on targets; and narrowband interference [8], which generates fake peak in the frequency spectrum and leads to false detections of non-existent targets (i.e., ghost targets). Compared to narrowband interference, wideband interference is much easier to occur.…”

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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Section: Introductionmentioning

confidence: 99%