信号处理2026，Vol.42Issue(8)：1259-1272,14.DOI:10.12466/xhcl.2026.08.006
频谱信号相似度分割与非法用频监测
Spectrum Signal Similarity-Based Segmentation and Illegal Transmission Monitoring
摘要
Abstract
With the rapid advancement of wireless communication technologies,spectrum resources have become in-creasingly scarce.Illegal occupancy behaviors—such as abnormal frequency bursts,ultra-wideband interference,and high-power malicious transmissions—have further exacerbated spectrum congestion,severely threatening the security and reliability of legitimate communication systems.In practical spectrum monitoring,owing to blurred parameter boundaries between legitimate and illegal signals and the difficulty in constructing a comprehensive prior knowledge base of legitimate signals,existing fixed-rule detection methods(for example,local outlier factor and long short-term memory(LSTM))face limitations in applicability.To address these challenges,this study proposes a spectrum signal similarity segmentation and illegal frequency usage monitoring algorithm.The method first separates signals in dense sig-nal environments through time-frequency trajectory clustering.Then,the isolation distributional kernel(IDK)is intro-duced to measure the distributional similarity between signals more accurately,and the Pettitt change-point detection al-gorithm is employed for adaptive segmentation of the wideband spectrum to identify abrupt changes in the frequency us-age behavior.On this foundation,similarity clustering is applied to automatically construct and update the legitimate sig-nal library,significantly reducing reliance on manual labeling.Finally,the copula-based outlier detection(COPOD)al-gorithm is utilized to model complex dependencies among features,enabling an efficient anomaly analysis of real-time wideband radio frequency usage behaviors.Simulation results demonstrated that the legitimate signal library constructed by the proposed method achieved high accuracy and effectively identified legitimate signal patterns.In illegal signal de-tection tasks,compared with traditional methods such as LSTM,one-class support vector machine(OSVM),and gen-erative adversarial network(GAN),the proposed algorithm exhibited superior performance in both false alarm rate and missed detection rate,offering an effective solution for building adaptive and intelligent spectrum monitoring systems.关键词
频谱监测/相似度分割/非法用频监测/异常检测Key words
spectrum monitoring/similarity segmentation/illegal frequency usage monitoring/anomaly detection分类
信息技术与安全科学引用本文复制引用
黄李浩,汤宇曦,程郁凡..频谱信号相似度分割与非法用频监测[J].信号处理,2026,42(8):1259-1272,14.基金项目
国家自然科学基金重点项目(62131005) （62131005）
中央高校基本科研业务费专项资金资助(2242022k60006) The National Natural Science Foundation of China(62131005) （2242022k60006）
The Fundamental Research Funds for the Central Universities(2242022k60006) （2242022k60006）
