A Truncated Matched Filter Method for Interrupted Sampling Repeater Jamming Suppression Based on Jamming Reconstruction

Lu, Lu; Wei, Xueyao

doi:10.3390/rs14010097

Cited by 11 publications

(1 citation statement)

References 28 publications

(20 reference statements)

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“…This approach leverages the interference's distinct energy distribution characteristics to enhance the interference suppression's effectiveness. Lu et al [22] introduced a truncated matched filter method to suppress ISRJ in LFM radars by capitalizing on the distinct energy differences between interference and target echoes in the matched filter results.…”

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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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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Deep reinforcement learning based decision making for radar jamming suppression

Xiao,

Cao,

et al. 2024

Digital Signal Processing

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Interrupted sampling repeater jamming suppression based on multiple extended complex‐valued convolutional auto‐encoders

Meng,

Yu,

Wei

2024

IET Radar Sonar & Navi

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Interrupted sampling repeater jamming (ISRJ) with flexible modulation parameters and coherent processing gain seriously threatens the radar detection system. The jamming suppression and target detection performance of existing anti‐jamming methods are limited by strong noise and jamming signals. An ISRJ suppression method combining multiple extended complex‐valued convolutional auto‐encoders (CVCAEs) and compressed sensing (CS) reconstruction is proposed. For the different tasks of parameter estimation and signal denoising, the extended CVCAEs including a complex‐valued convolutional shrinkage network (CVCSNet) and a complex‐valued UNet (CVUNet) are developed. Based on the time‐domain discontinuity of ISRJ signals, the CVCSNet is first used to directly estimate the parameters representing signal components and extract jamming‐free signals from received signals. Then, the extracted signals are denoised using the CVUNet. After that, relying on the denoised signals and the frequency sparsity of de‐chirped target signals, a CS model is established and solved to recover complete target signals for jamming suppression. Utilising the advantages of deep neural networks in weak feature extraction and signal representation, the CVCSNet and CVUNet can effectively improve the signal extraction accuracy and alleviate the limitation of noise on target signal reconstruction. Experimental results verify that the proposed method has superior ISRJ suppression performance and is robust to varying signal‐to‐noise ratios, jamming‐to‐signal ratios and jamming parameters.

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A Truncated Matched Filter Method for Interrupted Sampling Repeater Jamming Suppression Based on Jamming Reconstruction

Cited by 11 publications

References 28 publications

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

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

Deep reinforcement learning based decision making for radar jamming suppression

Interrupted sampling repeater jamming suppression based on multiple extended complex‐valued convolutional auto‐encoders

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