期刊
ELECTRONICS
卷 12, 期 22, 页码 -出版社
MDPI
DOI: 10.3390/electronics12224598
关键词
underwater mine detection; acoustic wireless sensor network; clustered UWSNs; wavelet transformation; sonar signal
Underwater mines pose a significant threat to aquatic life, submarines, and naval activities. Deploying underwater acoustic sensor networks can provide an efficient solution, but energy consumption is a concern. This study proposes an energy-efficient mine detection solution based on clustering, achieving high accuracy.
Underwater mines are considered a major threat to aquatic life, submarines, and naval activities. Detecting and locating these mines is a challenging task, due to the nature of the underwater environment. The deployment of underwater acoustic sensor networks (UWASN) can provide an efficient solution to this problem. However, the use of these self-powered sensors for intensive data sensing and wireless communication is often energy-scaring and might call into question the viability of their application. One attractive solution to extend the underwater wireless sensor network will be the adoption of cluster-based communication, since data processing and communication loads are distributed in a timely manner over the members of the cluster. In this context, this study proposes an energy-efficient solution for high-accuracy underwater mine detection based on the adequate clustering approach. The proposed scheme uses a processing approach based on wavelet transformation to extract relevant features to efficiently distinguish mines from other objects using the Naive Bayes algorithm for classification. The main novelty of this approach is the design of a new low-complexity scheme for efficient sensor-based acoustic object detection that outperforms most of the existing solutions. It consumes a low amount of energy, while ensuring 95.12% target detection accuracy.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据