An ISRJ suppression method based on time-frequency analysis combined with frequency-dimension projection

IF 3 3区工程技术 Q2 ENGINEERING, ELECTRICAL & ELECTRONIC

Digital Signal Processing Pub Date : 2025-09-06 DOI:10.1016/j.dsp.2025.105599

Jun Luo , Hui Chen , Weijian Liu , Binbin Li , Pei Tian , Xiaoge Wang

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引用次数: 0

Abstract

To effectively suppress interrupted sampling repeater jamming (ISRJ), this paper proposes an ISRJ suppression method based on time-frequency analysis combined with frequency-dimension projection, leveraging the distinct time-frequency amplitude responses of target echo signals and ISRJ signals after dechirping. Firstly, a bidirectional sliding window detection method is used to locate the echo pulses. Subsequently, the time-frequency matrix of the localized region is projected onto the frequency dimension, and a differential method is applied to preliminarily identify peak points of jamming and targets. Then, the standard deviation from statistics is utilized as a discriminant metric to distinguish between target and jamming peaks, followed by the construction of a time-frequency filter for jamming suppression. Finally, performing inverse Short-Time Fourier Transform (STFT) processing on the jamming-suppressed time-frequency matrix to obtain the final suppression results. Simulation experiments demonstrate the proposed algorithm exhibiting superior jamming suppression performance, particularly addressing the limitations of traditional methods under low signal-to-noise ratio (SNR) conditions.

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基于时频分析与频率维投影相结合的ISRJ抑制方法

为了有效抑制中断采样中继器干扰（ISRJ），本文提出了一种基于时频分析与频率维投影相结合的ISRJ抑制方法，利用目标回波信号与ISRJ信号解调后时频振幅响应的差异。首先，采用双向滑动窗检测方法对回波脉冲进行定位；然后，将局域区域的时频矩阵投影到频率维上，采用微分法初步识别干扰和目标的峰值点。然后，利用统计量的标准差作为区分目标和干扰峰的判别指标，构建时频滤波器抑制干扰。最后，对抑制干扰的时频矩阵进行短时傅里叶反变换（STFT）处理，得到最终的抑制结果。仿真实验表明，该算法具有优异的干扰抑制性能，特别解决了传统方法在低信噪比条件下的局限性。

本文章由计算机程序翻译，如有差异，请以英文原文为准。

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来源期刊

Digital Signal Processing 工程技术-工程：电子与电气

CiteScore

5.30

自引率

17.20%

发文量

435

审稿时长

66 days

期刊介绍： Digital Signal Processing: A Review Journal is one of the oldest and most established journals in the field of signal processing yet it aims to be the most innovative. The Journal invites top quality research articles at the frontiers of research in all aspects of signal processing. Our objective is to provide a platform for the publication of ground-breaking research in signal processing with both academic and industrial appeal. The journal has a special emphasis on statistical signal processing methodology such as Bayesian signal processing, and encourages articles on emerging applications of signal processing such as: • big data• machine learning• internet of things• information security• systems biology and computational biology,• financial time series analysis,• autonomous vehicles,• quantum computing,• neuromorphic engineering,• human-computer interaction and intelligent user interfaces,• environmental signal processing,• geophysical signal processing including seismic signal processing,• chemioinformatics and bioinformatics,• audio, visual and performance arts,• disaster management and prevention,• renewable energy,