Wide‐band interference mitigation algorithm for SAR based on time‐varying filtering and sparse recovery

Lu, Xingyu; Yang, Jianchao; Ma, Chao; Gu, Hong; Su, Weimin

doi:10.1049/el.2017.3963

Cited by 14 publications

(11 citation statements)

References 5 publications

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“…Nguyen et al adopted similar assumptions for sparse representation and recovery framework based on processing each range-compressed record independently [79]. Lu et al extends this idea by applying the sparse recovery on the time-frequency plane [80].…”

Section: Reconstructionmentioning

confidence: 99%

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

et al. 2019

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Radio frequency interference (RFI) is a major issue in accurate remote sensing by a synthetic aperture radar (SAR) system, which poses a great hindrance to raw data collection, image formation, and subsequent interpretation process. This paper provides a comprehensive study of the RFI mitigation techniques applicable for an SAR system. From the view of spectrum allocation, possible terrestrial and spaceborne RFI sources to SAR system and their geometry are analyzed. Typical signal models for various RFI types are provided, together with many illustrative examples from real measured data. Then, advanced signal processing techniques for removing RFI are reviewed. Advantages and drawbacks of each approach are discussed in terms of their applicability. Discussion on the future trends are provided from the perspective of cognitive, integrated, and adaptive. This review serves as a reference for future work on the implementation of the most suitable RFI mitigation scheme for an air-borne or space-borne SAR system.

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Section: Reconstructionmentioning

confidence: 99%

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

et al. 2019

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“…In view of the difficulty of exact estimation in multi-parameter mathematical models, another strategy of the model-driven RFI mitigation algorithms is introduced by virtue of low-rank matrix approximation and canonical constraints. This strategy utilizes the hypothesis of the low-rank and sparse properties for different components in the SAR data [26][27][28][29][30][31][32][33][34][35], and is realized by the different ordering norms in some representation domains. It significantly reduces the difficulty and complexity of parameter estimation compared with the multi-parameter mathematical model.…”

Section: Previous Work On Rfi Mitigationmentioning

confidence: 99%

Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties

et al. 2022

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Synthetic aperture radar (SAR) is susceptible to radio frequency interference (RFI), which becomes especially commonplace in the increasingly complex electromagnetic environments. RFI severely detracts from SAR imaging quality, which hinders image interpretation. Therefore, some RFI mitigation algorithms have been introduced based on the partial features of RFI, but the RFI reconstruction models in these algorithms are rough and can be improved further. This paper proposes two algorithms for accurately modeling the structural properties of RFI and target echo signal (TES). Firstly, an RFI mitigation algorithm joining the low-rank characteristic and dual-sparsity property (LRDS) is proposed. In this algorithm, RFI is treated as a low-rank and sparse matrix, and the sparse matrix assumption is made for TES in the time–frequency (TF) domain. Compared with the traditional low-rank and sparse models, it can achieve better RFI mitigation performance with less signal loss and accelerated algorithm convergence. Secondly, the other RFI mitigation algorithm, named as TFC-LRS, is proposed to further reduce the signal loss. The TF constraint concept, in lieu of the special sparsity, is introduced in this algorithm to describe the structural distribution of RFI because of its aggregation characteristic in the TF spectrogram. Finally, the effectiveness, superiority, and robustness of the proposed algorithms are verified by RFI mitigation experiments on the simulated and measured SAR datasets.

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“…A parameter estimation method using TF analysis and deconvolution of ISRJ is proposed in [16], and ISRJ is suppressed by interference cancellation through the reconstruction of the jamming signal. Based on signal sparse recovery, [17,18] propose new ideas for jamming suppression. A wideband jamming suppression method based on TF domain filtering and sparse recovery is verified in [17].…”

Section: Introductionmentioning

confidence: 99%

“…Based on signal sparse recovery, [17,18] propose new ideas for jamming suppression. A wideband jamming suppression method based on TF domain filtering and sparse recovery is verified in [17]. In [18], the energy function is first used to extract the signal part not disturbed by ISRJ; then, the target reconstruction is completed based on the sparsity of the signal in the frequency domain after dechirping.…”

Section: Introductionmentioning

confidence: 99%

Neural Network-Guided Sparse Recovery for Interrupted-Sampling Repeater Jamming Suppression

Wang

et al. 2021

International Journal of Antennas and Propagation

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Interrupted-sampling repeater jamming (ISRJ) is a new type of DRFM-based jamming designed for linear frequency modulation (LFM) signals. By intercepting the radar signal slice and retransmitting it many times, ISRJ can obtain radar coherent processing gain so that multiple false target groups can be formed after pulse compression (PC). According to the distribution characteristic of the echo signal and the coherence of ISRJ to radar signal, a new method for ISRJ suppression is proposed in this study. In this method, the position of the real target is determined using a gated recurrent unit neural network (GRU-Net), and the real target can be, therefore, reconstructed by adaptive filtering in the sparse representation of the echo signal based on the target locating result. The reconstruction result contains only the real target, and the false target groups formed by ISRJ are suppressed completely. The target locating accuracy of the proposed GRU-Net can reach 92.75%. Simulations have proved the effectiveness of the proposed method.

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Wide‐band interference mitigation algorithm for SAR based on time‐varying filtering and sparse recovery

Cited by 14 publications

References 5 publications

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

Mitigation of Radio Frequency Interference in Synthetic Aperture Radar Data: Current Status and Future Trends

Radio Frequency Interference Mitigation for Synthetic Aperture Radar Based on the Time-Frequency Constraint Joint Low-Rank and Sparsity Properties

Neural Network-Guided Sparse Recovery for Interrupted-Sampling Repeater Jamming Suppression

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