## 2 Interference Signal Models
### Principles of the FCME Algorithm and the Proposed Algorithm  
FCME Algorithm  
First, the algorithm sorts the FFT-transformed data of length \(N\) in ascending order, selecting the first 10% of frequencies as the noise set, while the remaining frequencies constitute the interference set. The initial threshold is then calculated based on the amplitudes of the frequencies in the noise set. Next, the amplitudes of the frequencies in the interference set are compared one by one with the initial threshold. Any frequencies with amplitudes smaller than the threshold are moved to the noise set, while those with larger amplitudes remain in the interference set. The threshold is then recalculated using the updated noise set, and the amplitudes of the frequencies in the updated interference set are compared with this updated threshold. The process iterates until no frequencies in the interference set have amplitudes smaller than the threshold, or the maximum number of iterations is reached, at which point the algorithm stops.  
However, this algorithm requires sorting during the initial threshold calculation process. When the number of samples is large, this leads to high computational complexity and a large amount of computation. The limited memory and computational capability of satellite payloads make it difficult to support the efficient operation of this algorithm.  
2. Proposed Algorithm  
This paper improves upon the limitations of the FCME algorithm by proposing an efficient differential amplitude interference detection algorithm based on non-uniform adaptive segmentation. The proposed algorithm first divides the FFT-transformed data of length \(N\) into \(p\) segments and uses the differential method to identify a relatively flat noise segment. The noise segment is then further subdivided into \(n\) segments, and the differential method is applied again to accurately locate the final flat noise segment. The data from this flat noise segment are used to calculate the initial threshold. Next, the algorithm utilizes non-uniform data segments \(L_{1},L_{2},\ldots\ldots,Q_{1},Q_{2},\ldots\ldots,Q_{i},\ldots\ldots,Q_{n}, \ldots\ldots,L_{p}\), \(1~{}<~{}i~{}<~{}n\) to calculate the mean amplitude of each segment, and these values are compared one by one with the initial threshold to classify the noise and interference sets.  
Figure 1: Schematic comparison of the principles of the FCME algorithm and the proposed algorithm.  
The final threshold is obtained through continuous iterations. Finally, all frequencies are compared with the final threshold and those with amplitudes greater than the threshold are classified as interference frequencies.  
The proposed algorithm fully leverages the spectral characteristics of the signal by dividing it into segments of varying lengths. A two-step differential method is employed to dynamically identify the flat noise segment for initial threshold calculation and performs interference detection through segment-wise iterations. Compared to the FCME algorithm, this approach simplifies the initial threshold calculation and iterative process, making it more suitable for satellite communication systems with limited computational resources.