Prior-Guided Deep Interference Mitigation for FMCW Radars

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

doi:10.1109/tgrs.2022.3211605

Cited by 12 publications

(5 citation statements)

References 35 publications

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“…To assess the effectiveness of the proposed method, a range of recently proposed techniques, including wavelet denoising [15], ANC [16], an attention-based bidirectional GRU (BiGRU) [24], and a complex-valued CNN [26], are chosen for conducting a comparative analysis. The latter three methods are trained for the same number of epochs based on simulated FMCW radar signals.…”

Section: Performance Comparisonmentioning

confidence: 99%

“…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. In [27], dilated convolution was used to achieve improved interference mitigation performance.…”

Section: Introductionmentioning

confidence: 99%

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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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Section: Performance Comparisonmentioning

confidence: 99%

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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“…Chen et al [31] proposed an adaptive multi-scale residual auto-encoder to suppress interference in short-range detection systems. In [32], a complex-valued fully convolutional network was proposed to solve the interference mitigation problem of frequency-modulated continuous wave (FMCW) radar. In [33], the weights and activations of anti-interference networks were quantitatively discussed and analyzed.…”

Section: Introductionmentioning

confidence: 99%

An Attention-Guided Complex-Valued Transformer for Intra-Pulse Retransmission Interference Suppression

Wang,

Li,

Zhou

et al. 2024

Remote Sensing

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With the maturation of digital radio frequency memory (DRFM) technology, various intra-pulse retransmission interference methods have emerged. These flexible and changeable retransmission interference methods pose significant challenges to radar detection tasks, particularly in modern battlefields. This paper proposes an attention-guided complex-valued transformer (AGCT) as a solution. First, the encoder maps the received signal contaminated by interference and noise into a high-dimensional space. Then, the dilated convolution block (DCB) group and attention block (AB) group in the mask estimator extract the delicate multi-scale features and large-scale features of the interference, respectively, to obtain a multidimensional space mask. Finally, the decoder restores interference to the time domain and outputs the estimated target echo using residual learning. Considering the characteristics of intra-pulse interference, we introduced the energy attention block (EAB) at the end of the DCBs and the ABs within our network. This addition ensures a heightened focus on extracting interference features. Furthermore, we implemented a curriculum learning strategy during the network training. This approach gradually acclimates the network to fit different types of retransmission interference, starting from simpler to more complex scenarios. Our extensive experiments, conducted under various conditions, have provided compelling evidence of the AGCT’s superior performance. Compared to the comparative network, the AGCT’s advantages are particularly pronounced under more harsh conditions, demonstrating its robustness and effectiveness.

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“…In Ref. [ 40 ], the interference mitigation problem was treated as a regression issue and a method for interference mitigation, based on complex-valued convolutional neural networks, was proposed, which processes the time–frequency distribution of radar beat signals to achieve interference suppression. In Ref.…”

Section: Introductionmentioning

confidence: 99%

An Interference Mitigation Method for FMCW Radar Based on Time–Frequency Distribution and Dual-Domain Fusion Filtering

Zhou,

Cao,

Zhang

et al. 2024

Sensors

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Radio frequency interference (RFI) significantly hampers the target detection performance of frequency-modulated continuous-wave radar. To address the problem and maintain the target echo signal, this paper proposes a priori assumption on the interference component nature in the radar received signal, as well as a method for interference estimation and mitigation via time–frequency analysis. The solution employs Fourier synchrosqueezed transform to implement the radar’s beat signal transformation from time domain to time–frequency domain, thus converting the interference mitigation to the task of time–frequency distribution image restoration. The solution proposes the use of image processing based on the dual-tree complex wavelet transform and combines it with the spatial domain-based approach, thereby establishing a dual-domain fusion interference filter for time–frequency distribution images. This paper also presents a convolutional neural network model of structurally improved UNet++, which serves as the interference estimator. The proposed solution demonstrated its capability against various forms of RFI through the simulation experiment and showed a superior interference mitigation performance over other CNN model-based approaches.

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Prior-Guided Deep Interference Mitigation for FMCW Radars

Cited by 12 publications

References 35 publications

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

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

An Attention-Guided Complex-Valued Transformer for Intra-Pulse Retransmission Interference Suppression

An Interference Mitigation Method for FMCW Radar Based on Time–Frequency Distribution and Dual-Domain Fusion Filtering

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