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FU Zewen, WEI Tingting, LI Ningning, LI Ning. Detection and Localization of Radio Frequency Interference via Cross-domain Multi-feature from SAR Raw Data[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250701
Citation: FU Zewen, WEI Tingting, LI Ningning, LI Ning. Detection and Localization of Radio Frequency Interference via Cross-domain Multi-feature from SAR Raw Data[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250701

Detection and Localization of Radio Frequency Interference via Cross-domain Multi-feature from SAR Raw Data

doi: 10.11999/JEIT250701 cstr: 32379.14.JEIT250701
Funds:  The Natural Science Foundation of Henan (242300421170)
  • Received Date: 2025-07-28
  • Rev Recd Date: 2025-09-30
  • Available Online: 2025-10-11
  •   Objective  The increasing congestion of the electromagnetic spectrum presents major challenges for Synthetic Aperture Radar (SAR) systems, where Radio Frequency Interference (RFI) can severely degrade imaging quality and compromise interpretation accuracy. Existing detection methods have critical limitations: time-domain approaches are insensitive to weak interference, whereas transform-domain methods perform poorly in characterizing broadband interference. This study develops a cross-domain framework that integrates complementary features from multiple domains, enabling robust RFI detection and accurate localization. The proposed approach addresses the deficiencies of single-domain methods and provides a reliable solution for operational SAR systems.  Methods  This study introduces two methodological innovations. First, a weighted feature fusion framework combines the first-order derivatives of time-domain kurtosis and skewness using Principal Component Analysis (PCA)-optimized weights, thereby capturing both global statistical distributions and local dynamic variations. Second, a differential time–frequency analysis technique applies the Short-Time Fourier Transform (STFT) with logarithmic ratio operations and adaptive thresholding to achieve sub-pulse interference localization. The overall workflow integrates K-means clustering for initial detection, STFT-based feature enhancement, binary region identification, and Inverse STFT (ISTFT) reconstruction. The proposed approach is validated against three state-of-the-art methods using both simulated data and Sentinel-1 datasets.  Results and Discussions  Experimental results demonstrate marked improvements across all evaluation metrics. For simulated data, the proposed method achieves a signal accuracy (SA) of 98.56% and a False Alarm (FA) rate of 0.65% (Table 2), representing a 3.13% gain in SA compared with conventional methods. The Root Mean Square Error (RMSE) reaches 0.1902 (Table 3), corresponding to a 10.9% improvement over existing techniques. Visual analysis further confirms more complete interference detection (Fig. 2) and cleaner suppression results (Figs. 4 and 7), with target features preserved. For measured data, the method maintains robust performance, achieving a gray entropy of 0.7843 (Table 5), and effectively mitigating the severe FAs observed in traditional approaches (Fig. 8).  Conclusions  In complex and dynamic electromagnetic environments, traditional RFI detection methods often show inaccuracies or even fail when processing Narrowband Interference (NBI) or Wideband Interference (WBI), limiting their operational applicability. To address this challenge, this study proposes an engineering-oriented interference detection method designed for practical SAR operations. By combining time-domain kurtosis with the first derivative of skewness, the approach significantly enhances detection accuracy and adaptability. Furthermore, a localization technique is introduced that precisely identifies interference positions. Using time–frequency domain analysis, the method calculates differential values between the time–frequency representations of echo signals with and without interference, and determines interference locations through threshold-based judgment. Extensive simulations and Sentinel-1 experiments confirm the universality and effectiveness of the proposed method in both detection and localization.
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  • [1]
    ZHOU Feng and TAO Mingliang. Research on methods for narrow-band interference suppression in synthetic aperture radar data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2015, 8(7): 3476–3485. doi: 10.1109/JSTARS.2015.2431916.
    [2]
    LI Ning and HU Xingwang. Ultrawideband mutual RFI mitigation between SAR satellites: From the perspective of European sentinel-1A[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5229020. doi: 10.1109/TGRS.2024.3501309.
    [3]
    ZHANG Zhizheng, SHU Gaofeng, HUANG Yabo, et al. Screening and artifact detection of RFI in sentinel-1A time-series images combining change detection techniques with structural similarity index[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18: 10864–10881. doi: 10.1109/JSTARS.2025.3559171.
    [4]
    ZHOU Yashi, WANG Pei, CHEN Zhen, et al. Very high resolution SAR imaging with DGPS-supported airborne X-band data[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2020, 13: 3605–3617. doi: 10.1109/JSTARS.2020.3004013.
    [5]
    LI Ning, ZHANG Hengrui, LV Zongsen, et al. Simultaneous screening and detection of RFI from massive SAR images: A case study on european sentinel-1[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5231917. doi: 10.1109/TGRS.2022.3191815.
    [6]
    LYU Qiyuan, HAN Bing, LI Guangzou, et al. SAR interference suppression algorithm based on low-rank and sparse matrix decomposition in time–frequency domain[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4008305. doi: 10.1109/LGRS.2020.3048161.
    [7]
    舒高峰, 刘明月, 李宁. 采用改进特征子空间投影的SAR脉冲式直达波干扰抑制方法[J]. 电子与信息学报, 2024, 46(4): 1382–1390. doi: 10.11999/JEIT230665.

    SHU Gaofeng, LIU Mingyue, and LI Ning. SAR pulsed direct wave interference suppression method using improved Eigen-subspace projection[J]. Journal of Electronics & Information Technology, 2024, 46(4): 1382–1390. doi: 10.11999/JEIT230665.
    [8]
    庄学彬, 牛犇, 林子健, 等. 基于门控机制的并行CNN-Transformer神经网络的多参数欺骗干扰检测方法[J]. 电子与信息学报, 2025, 47(6): 2005–2014. doi: 10.11999/JEIT240977.

    ZHUANG Xuebin, NIU Ben, LIN Zijian, et al. A multiparameter spoofing detection method based on parallel CNN-transformer neural network with gating mechanism[J]. Journal of Electronics & Information Technology, 2025, 47(6): 2005–2014. doi: 10.11999/JEIT240977.
    [9]
    BUCKREUSS S and HORN R. E-SAR P-band SAR subsystem design and RF-interference suppression[C]. Proceedings of the Sensing and Managing the Environment. 1998 IEEE International Geoscience and Remote Sensing. Symposium Proceedings, Seattle, USA, 1998: 466–468. doi: 10.1109/IGARSS.1998.702941.
    [10]
    ZHOU Feng, WU Renbiao, XING Mengdao, et al. Eigensubspace-based filtering with application in narrow-band interference suppression for SAR[J]. IEEE Geoscience and Remote Sensing Letters, 2007, 4(1): 75–79. doi: 10.1109/LGRS.2006.887033.
    [11]
    FENG Jin, ZHENG Huifang, DENG Yunkai, et al. Application of subband spectral cancellation for SAR narrow-band interference suppression[J]. IEEE Geoscience and Remote Sensing Letters, 2012, 9(2): 190–193. doi: 10.1109/LGRS.2011.2163150.
    [12]
    LI Ning, LV Zongsen, GUO Zhengwei, et al. Time-domain notch filtering method for pulse RFI mitigation in synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4013805. doi: 10.1109/LGRS.2021.3077247.
    [13]
    郑慧芳, 杨淋, 冯锦. SAR窄带干扰抑制的子带子空间滤波技术研究[J]. 电子与信息学报, 2013, 35(12): 2836–2842. doi: 10.3724/SP.J.1146.2013.00201.

    ZHENG Huifang, YANG Lin, and FENG Jin. Research on the subband subspace filtering for narrow band interference suppression in SAR[J]. Journal of Electronics & Information Technology, 2013, 35(12): 2836–2842. doi: 10.3724/SP.J.1146.2013.00201.
    [14]
    LI Ning, LV Zongsen, and GUO Zhengwei. Pulse RFI mitigation in synthetic aperture radar data via a three-step approach: Location, notch, and recovery[J]. IEEE Transactions on Geoscience and Remote Sensing, 2022, 60: 5225617. doi: 10.1109/TGRS.2022.3161368.
    [15]
    ZHANG Hengrui and LI Ning. Composite indicator for detecting and localizing time-varying RFI in SAR raw data[J]. IEEE Transactions on Geoscience and Remote Sensing, 2024, 62: 5101114. doi: 10.1109/TGRS.2023.3345151.
    [16]
    WEI Tingting, HU Xingwang, GUO Zhengwei, et al. A two-stage method for screening pulse RFI in SAR raw data alternating the use of time and frequency domains[J]. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2025, 18: 4331–4346. doi: 10.1109/JSTARS.2025.3530989.
    [17]
    HASHIMOTO Y, HIROSE A, and NATSUAKI R. Degree-of-polarization-based radio frequency interference detection for synthetic aperture radar[J]. IEEE Transactions on Geoscience and Remote Sensing, 2025, 63: 5212015. doi: 10.1109/TGRS.2025.3570493.
    [18]
    DI VINCENZO A, NATALE A, BERARDINO P, et al. A new paradigm based on the Bayesian information criterion for the detection of radio frequency interferences in SAR data[C]. Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024: 7948–7951. doi: 10.1109/IGARSS53475.2024.10642440.
    [19]
    HASHIMOTO Y, NATSUAKI R, and HIROSE A. RFI detection using degree of polarization for polarimetric synthetic aperture radar[C]. Proceedings of 2024 IEEE International Geoscience and Remote Sensing Symposium, Athens, Greece, 2024: 11482–11485. doi: 10.1109/IGARSS53475.2024.10640790.
    [20]
    LV Zongsen, ZHANG Zhimin, FAN Huaitao, et al. A two-stage approach for TSNB and ITWB RFI mitigation in P- and L-band SAR data[J]. IEEE Transactions on Aerospace and Electronic Systems, 2024, 60(2): 1450–1470. doi: 10.1109/TAES.2023.3336641.
    [21]
    DAVIS M E. Frequency allocation challenges for ultra-wideband radars[J]. IEEE Aerospace and Electronic Systems Magazine, 2013, 28(7): 12–18. doi: 10.1109/MAES.2013.6559376.
    [22]
    LI Ning, LV Zongsen, GUO Zhengwei, et al. Time-domain notch filtering method for pulse RFI mitigation in synthetic aperture radar[J]. IEEE Geoscience and Remote Sensing Letters, 2022, 19: 4013805. doi: 10.1109/LGRS.2021.3077247. (查阅网上资料,本条文献与第12条文献重复,请确认).
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