A Two-Stage DNN Model With Mask-Gated Convolution for Automotive Radar Interference Detection and Mitigation

Chen, Shengyi; Taghia, Jalal; Kühnau, Uwe; Pohl, Nils; Martin, Rainer

doi:10.1109/jsen.2022.3173129

Cited by 10 publications

(2 citation statements)

References 31 publications

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“…However, as the cut-out zone in the signal increases, the accuracy of the recovered signals decreases significantly. Recently some deep-learning approaches have been used for interference mitigation of FMCW radars, such as using recurrent neural network [16], convolutional neural network [17], [18], two-stage deep neural network [19], and autoencoder [20]. These methods seem promising to provide accurate results but for the training process, these methods often require an extensive dataset collected in various settings since the generality of those approaches needs to be confirmed thoroughly.…”

Section: Introductionmentioning

confidence: 99%

Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform

Asmaur Rohman,

Suryadi Satyawan,

Kurniawan

et al. 2024

IJECE

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Radar is one of the sensors that have significant attention to be implemented in an autonomous vehicle since its robustness under many possible environmental conditions such as fog, rain, and poor light. However, the implementation risks interference because of transmitting and/or receiving radar signals from/to other vehicles. This interference will increase the floor noise that can mask the target signal. This paper proposes multiplicative-adaptive filtering and Hilbert transform to mitigate the interference effect and maintain the target signal detectability. The method exploited the trade-off between the step-size and sidelobe effect on the least mean square-based adaptive filtering to improve the target detection accuracy, especially in the long-range case. The numerical analysis on the millimeter-wave frequency modulated continuous wave radar with multiple interferers concluded that the proposed method could maintain and enhance the target signal even if the target range is relatively far from the victim radar.

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Section: Introductionmentioning

confidence: 99%

Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform

Asmaur Rohman,

Suryadi Satyawan,

Kurniawan

et al. 2024

IJECE

View full text Add to dashboard Cite

show abstract

“…In some studies, time-domain signals were subjected to Fourier transforms and converted into two-dimensional data for processing with a convolutional neural network (CNN). A twostage deep neural network (DNN) model with mask-gated convolution [25] was proposed for radar interference detection and mitigation. In [26], a prior-guided method based on a complex-valued CNN was introduced to effectively eliminate interference in the time-frequency domain.…”

Section: Introductionmentioning

confidence: 99%

VANET-Assisted Interference Mitigation for Millimeter-Wave Automotive Radar Sensors

Zhang

Yang

et al. 2020

IEEE Network

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Frequency-modulated continuous-wave (FMCW) radar plays a pivotal role in the field of remote sensing. The increasing degree of FMCW radar deployment has increased the mutual interference, which weakens the detection capabilities of radars and threatens reliability and safety of systems. In this paper, a novel FMCW radar interference mitigation (RIM) method, termed as RIMformer, is proposed by using an endto-end Transformer-based structure. In the RIMformer, a dual multi-head self-attention mechanism is proposed to capture the correlations among the distinct distance elements of intermediate frequency (IF) signals. Additionally, an improved convolutional block is integrated to harness the power of convolution for extracting local features. The architecture is designed to process time-domain IF signals in an end-to-end manner, thereby avoiding the need for additional manual data processing steps. The improved decoder structure ensures the parallelization of the network to increase its computational efficiency. Simulation and measurement experiments are carried out to validate the accuracy and effectiveness of the proposed method. The results show that the proposed RIMformer can effectively mitigate interference and restore the target signals.

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Automatic Multipath Annotation for Conventional Automotive Radar Datasets

Danino,

Bilik

2024

IEEE Sensors J.

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A Two-Stage DNN Model With Mask-Gated Convolution for Automotive Radar Interference Detection and Mitigation

Cited by 10 publications

References 31 publications

Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform

Robust automotive radar interference mitigation using multiplicative-adaptive filtering and Hilbert transform

VANET-Assisted Interference Mitigation for Millimeter-Wave Automotive Radar Sensors

Automatic Multipath Annotation for Conventional Automotive Radar Datasets

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