Detection and Localization of Radio Frequency Interference via Cross-domain Multi-feature from SAR Raw Data
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摘要: 合成孔径雷达(SAR)易受同频段电子设备产生的射频干扰影响，使SAR图像解译难度显著增加。射频干扰的检测与定位有利于将SAR回波数据中的干扰精准“剔除”，降低射频干扰对SAR图像解译的不利影响。针对射频干扰业务化、工程化的检测与定位，该文提出一种融合跨域多特征的SAR射频干扰检测与定位方法。首先，通过对时域矩峰度和时域一阶偏导数进行加权融合，实现回波信号的初步检测。然后，结合短时傅里叶变换与对数比差异变化能量检测技术，获取射频干扰在SAR回波数据时频域中的时空表征，最后，将射频干扰的时空表征映射至时域，得到射频干扰信号的精确位置。通过全方位多角度的仿真与实测数据实验，结果表明所提方法针对窄带干扰和宽带干扰的检测与定位精度均优于传统方法，为SAR系统工程化检测与定位射频干扰提供了可靠的技术方案。Abstract:
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) reaches0.1902 (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 and7 ), with target features preserved. For measured data, the method maintains robust performance, achieving a gray entropy of0.7843 (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. -
表 1 干扰信号参数设置
参数名称 参数值 载波频率 5.300 GHz 采样频率 32.317 MHz 平台有效速度 7000 m/s景中心斜距 988647 m脉冲重复频率 1256.98 Hz发射信号时宽 41.74 μs 发射信号带宽 30 MHz 干扰载频 5.305 GHz 干扰带宽 6.0 MHz 
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表 2 仿真数据不同检测结果定量分析(%)
复合算子方法 TFA 所提方法 SINR=-5 dB SA 95.43 97.34 98.56 FA 1.54 1.96 0.65 SINR=0 dB SA 94.89 96.89 98.03 FA 1.87 2.14 0.93 SINR=5 dB SA 94.07 95.97 97.48 FA 1.99 2.53 1.29
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表 4 窄带PRFI实测数据干扰抑制结果定量分析
方法 特征子空间 复合算子 所提方法 实测数据 灰度熵 0.583 7 0.558 9 0.530 2 对比度 1.193 5 1.204 3 1.210 8 平均梯度 214.18 215.37 216.94 运行时间(s) 80.12 96.23 150.36
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表 5 含宽带RFI实测数据干扰抑制结果定量分析
方法 特征子空间 复合算子 所提方法 实测数据 灰度熵 0.863 7 0.796 0 0.784 3 对比度 1.430 2 1.4115 1.439 2 平均梯度 198.16 196.35 199.94 运行时间(s) 90.11 105.26 180.76
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