加权有向关联网络构建与表征的水中目标远距离检测

Remote detection of underwater targets is a critical technology in ocean defense systems and has significant applications in both national defense and civilian domains. However, there is currently a lack of effective methods for long-range detection of underwater targets, especially without prior knowledge of the target. To address this issue, a new detection method based on weighted directed association network, is proposed, which represents the target signal detection problem as network topology by mapping the vector acoustic signal into the weighted directed association network. The long-range detection of underwater targets without prior knowledge is achieved through the analysis of network topology characteristics and the feature extraction. The proposed method is validated through the simulated and measured data. Compared with existing narrowband mutual spectrum detection and bubble entropy methods, the proposed method can detect the underwater targets with lower signal-to-noise ratios and achieve the long-range detection without prior knowledge. The proposed method has important practical significance and application prospects and can provide the effective technical support for underwater target detection in ocean defense and civilian fields.