## 1 Introduction  
With the increasing complexity of the space electromagnetic environment and the growing openness of satellite communication links, satellite communication systems are highly susceptible to various intentional and unintentional electromagnetic interference, which can severely degrade communication performance. Therefore, the adoption of effective communication anti-jamming technologies is of great importance. Interference detection aims to analyze the location, power, and other parameters of interference within the received signal, providing essential foundations for the formulation and implementation of subsequent anti-jamming strategies. However, most existing interference detection algorithms exhibit high complexity and substantial computational requirements; while the computational resources of satellite payloads are highly valuable, their processing capability and power consumption are strictly constrained. Therefore, from the perspective of reducing system resource consumption, research on lightweight and efficient interference detection algorithms not only provides sufficient prior information for anti-jamming measures, but also enhances the anti-jamming capability of satellite communication systems, thereby improving overall system reliability. At present, research on interference detection algorithms by scholars in the field is relatively extensive and primarily categorized into two major types: binary interference detection algorithms and frequency-domain interference detection algorithms. Binary interference detection algorithms include classical methods, such as energy detection algorithm [1; 2], cyclic autocorrelation detection algorithm [3; 4; 5], matched filtering detection algorithm [6; 7; 8], etc. However, these methods have inherent limitations, as they can only determine the presence of interference but cannot provide precise spectral location, power, and other key parameters of the interference signal. This limitation restricts their applicability in refined interference analysis and the formulation of anti-jamming strategies. To overcome this technical bottleneck, frequency-domain interference detection algorithms have been extensively studied and applied. [PERSON] et al. proposed the consecutive mean excision (CME) algorithm [9], which employs iterations to classify all samples into a noise set and an interference set [10]. However, since this method initially assumes that all samples belong to the noise set, it is not suitable for broadband interference detection. [PERSON] improved the CME algorithm and proposed the forward consecutive mean excision (FCME) algorithm [11], which optimizes the initial threshold calculation by sorting the amplitude of all samples in ascending order (taking the first 10% of the samples as noise set at first). However, the drawback of this algorithm is its high complexity [12]. To further enhance interference signal detection accuracy, reference [13] proposed the localization algorithm based on double-thresholding (LAD), which sets high and low thresholds to respectively prevent misclassification and missed detections of interference signals, thereby reducing the false alarm probability. Building upon this, reference [14] proposed the localization algorithm based on double-thresholding adjacent cluster combining (LAD-ACC), which addresses the signal segmentation problem by merging adjacent signal clusters. Additionally, reference [15] builds upon the double-threshold FCME algorithm by incorporating a predefined intermediate threshold to process the spectrum of the received signal, achieving effective detection of narrowband interference under high signal-to-noise ratio conditions. In recent years, deep learning has been extensively applied in the field of broadband interference signal detection. The study in [16] proposed a one-dimensional fully convolutional network (FCN), which was validated on real satellite signal data, demonstrating its effectiveness.