相似网络构建与表征的水下声信号检测

Underwater acoustic signal detection plays a crucial role in ocean defense systems and has broad applications in civilian domains. However, contemporary underwater acoustic signal detection methods need to be improved for effectiveness when prior information about the target is unavailable. This paper proposes a new algorithm - a similarity network - to address the challenge of underwater target detection in complex oceanic backgrounds. In this method, information geometry and complex network theory are combined, and the problem of measuring node similarity is converted into a geometric problem on a matrix manifold, wherein the similarity between data at different time scales is determined, and a network representation of the time series data is achieved. Concurrently, a graph signal processing theory is introduced to extract the hidden dynamic characteristics of the target signal, thereby achieving underwater acoustic signal detection without prior target information. Further, the effectiveness of this method is demonstrated through research and verification of the simulated and actual. Our results show that the similarity network method is superior to existing network construction and passive target detection methods, can detect underwater acoustic signals more effectively, and can achieve underwater acoustic signal detection without any prior target information.