Detection of Anomaly Signal with Low Power Spectrum Density Based on Power Information Entropy

Abstract

The low power spectral density characteristics of the direct sequence spread spectrum (DSSS) signals make it difficult to be detected in complex and variable electromagnetic environments. Especially when DSSS signals as an intrusion signal are transmitted in channels overlapped by strong power signals, the possibility of DSSS signals being detected is very low. The traditional DSSS signal detection algorithm is only based on Gaussian white noise, the research scene is single and the complexity of the algorithm is high. In this paper, we will propose an electromagnetic spectrum intrusion signal detection algorithm based on signal power information entropy. According to the characteristics of the DSSS signal, the signal power information entropy is used as a feature, and a single class support vector machine (OC-SVM) is used as a classifier for anomaly signal detection. The simulation results show that the algorithm has the advantages of robustness, high efficiency, and low complexity.