Deep Learning for Interference Mitigation in Time-Frequency Maps of FMCW Radars

Li, Runlong; Wang, Jianping; He, Yuan; Yang, Yang; Lang, Yue

doi:10.1109/radar53847.2021.10028226

Cited by 2 publications

(2 citation statements)

References 9 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%

“…The interference mitigation in CS automotive radars via signal reconstruction based on autoregressive (AR) models in fast-and slow-time was proposed in [17]. 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].…”

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%

Section: Introductionmentioning

confidence: 99%

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

KOIZUMI,

WANG,

UMEHIRA

et al. 2024

IEICE Trans. Fundamentals

View full text Add to dashboard Cite

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Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

Reitz,

Künzle,

Franchi

et al. 2024

IEEE Access

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Frequency-modulated continuous-wave (FMCW) radars are becoming increasingly important in industrial applications due to their low cost, simple signal processing, and high resolution. The research is driven by the growing trend of radar applications in industrial settings, including tasks like autonomous robot navigation, radar-based monitoring of humans, and enhancing safety across various scenarios, such as smart factories, traffic control, or environmental monitoring. The network of distributed sensor nodes is a feature to be implemented by the sixth generation of wireless mobile networks. As a result, 6G will become an enabler technology that will develop even further use cases and advance the expansion of these technologies. Therefore, the mutual interference of these sensor systems is becoming highly relevant since they can be avoided by scheduling the individual resources of the sensor nodes by a network structure. This paper investigates the interference behavior of FMCW radar sensors in a dense indoor scenario. In these environments, in particular, the reliability of the sensor system plays an important role, and disruption can have critical consequences like not recognizing a target, triggering a false alarm, or the complete failure of the sensing. To correctly classify the expected interference in the overall context, the paper investigates the physical layer effects on the performance, such as multipath propagation, channel spread, delay, Doppler shift, and attenuation. By evaluating these effects using state-of-the-art signal processing, it was revealed that a high channel spread in particular and the dynamics of the radar channel can significantly reduce the detection quality, as the interference power can exceed the received power by up to 20 dB, depending on the scenario. The simulation results given in this paper show that a potential communication between the radars and compliance with a synchronization delay, the interference problems can be reliably prevented with an appropriately designed anti-aliasing filter. Our simulations have quantified the detection performance under interference through the entire signal chain by evaluating the physical layer, the radar signal processing with non-idealities, and the hardware. The paper has investigated interference probability in indoor environments by examining radars with uncoordinated transmissions. Integrating findings from the physical propagation characteristics, the deviations in chirp durations, and the frequency of interference cases offers insights into the potential risk of interference.

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Deep Learning for Interference Mitigation in Time-Frequency Maps of FMCW Radars

Cited by 2 publications

References 9 publications

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

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

Evaluation of the Interference Performance of FMCW Radar Sensors in Dense Indoor Environments

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