Remote Passive Sonar Detection by Relative Multiscale Change Entropy

Abstract

Remote passive sonar detection is significant as it constitutes potential real-time monitoring of severe underwater threats. There is still a lack of an efficient approach to achieving weak ship signal detection with nonparametric and noninformation priors. We propose the relative multiscale change entropy (RMCE) method to tackle these problems that should be promising. It measures the similarity between the signal under test and the ambient noise by combining different time scale factors to perceive the small changes in the complexity of the ambient noise, which is led by the presence of the distant ship. The value of RMCE is demonstrated by simulation and applied to the actual recorded data. We analyze the distribution characteristics of relative multiscale entropy of the ambient noise data with and without ship noise, collected in the South China Sea. Then this article presents a Neyman–Pearson (N–P) criterion-based RMCE detector for ship signal detection in the marine environment. The results show that the RMCE method outperforms the narrowband energy detection (NBED) method considerably.