Anomaly Detection of Small Targets on Sea Surface Based on Deep Graph Infomax

XU Shuwen; HE Qi; RU Hongtao

doi:10.11999/JEIT230887

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XU Shuwen, HE Qi, RU Hongtao. Anomaly Detection of Small Targets on Sea Surface Based on Deep Graph Infomax[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230887

Citation:

XU Shuwen, HE Qi, RU Hongtao. Anomaly Detection of Small Targets on Sea Surface Based on Deep Graph Infomax[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230887

XU Shuwen, HE Qi, RU Hongtao. Anomaly Detection of Small Targets on Sea Surface Based on Deep Graph Infomax[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230887

Citation:

XU Shuwen, HE Qi, RU Hongtao. Anomaly Detection of Small Targets on Sea Surface Based on Deep Graph Infomax[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT230887

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Anomaly Detection of Small Targets on Sea Surface Based on Deep Graph Infomax

doi: 10.11999/JEIT230887

National Laboratory of Radar Signal Processing, Xidian University, Xi’an 710071, China

Funds: The National Natural Science Foundation of China (61871303,62071346), The Fund for Foreign Scholars in University Research and Teaching Programs (The 111 Project) (B18039)

Received Date: 2023-08-11
Available Online: 2024-04-19

Abstract

Abstract

Due to the complex marine environment, it is difficult for a maritime radar to achieve high-performance detection of slow and small targets on the sea surface. For such targets, the traditional energy-based statistical detection algorithms suffer from serious performance loss. Confronted with this problem, a detection algorithm of small targets based on Deep Graph Infomax framework is proposed to realize unsupervised target anomaly detection in the background of sea clutter. In the traditional neural networks, there is an assumption that the samples are independent and identically distributed, which, however, the high-resolution radar echo does not meet. Therefore, this paper re-models the data from the perspective of graph and constructs the graph topological structure according to the correlation characteristics of the echo. Moreover, this paper puts forward the relative maximum node degree, and combines it with the relative average amplitude and the relative Doppler vector entropy to be the initial representation vectors of the graph nodes. With the graph modeling done, the graph attention network is used as the encoder in the Deep Graph Infomax framework to learn representation vectors. Finally, the anomaly detection algorithm is used to detect the targets, and the false alarm can be controlled. The detection result on the measured datasets shows that the performance of the proposed detector is improved by 9.2% compared to the three-feature detector when using the fast convex hull learning algorithm. Compared to the time-frequency three-feature detector, the performance is improved by 7.9%. When the network outputs a higher-dimensional representation vectors, the performance of the detector using the isolated forest algorithm is improved by 27.4%.
- Sea clutter,
- Target detection,
- Graph Neural Network,
- Unsupervised learning

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References(22)

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