相似网络构建与表征的水下声信号检测

Underwater Acoustic Signal Detection using Similarity Network Construction and Representation

水下声信号检测在海洋防御系统中扮演着不可或缺的角色,同时也广泛应用于民用领域。然而,在没有目标信号先验信息的情况下,目前仍缺乏行之有效的水下声信号检测方法。为此,该文提出了一种新的算法—相似网络,以解决在复杂海洋背景下水下目标检测的难题。该方法结合了信息几何和复杂网络理论,通过将节点相似度度量问题转化为矩阵流形上的几何问题,测量不同时间尺度上数据之间的相似性,并构建时间序列数据的网络表示。同时还引入了图信号处理理论,以提取目标信号内部隐藏的动力学特性,从而实现无目标先验信息下的水下声信号检测。通过对仿真和实测数据的研究验证,证明了该方法的有效性。结果表明,相似网络方法优于现有的网络构建和目标信号被动检测方法,能够更有效地检测水下声信号,实现无目标先验信息下的水下声信号检测。

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.