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Anomaly Detection in Satellite Using a New Self-Supervised Contrastive Learning Method based on Interleaved Cross Attention Networks
编号：96 访问权限：仅限参会人 更新：2025-11-10 15:17:47 浏览：8次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

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摘要
    In the domain of satellite anomaly detection using deep learning, traditional reconstruction-based methods often fall short when addressing the challenges posed by small sample problem and high sensitivity requirements. To overcome these limitations, this paper introduces a new method based on a contrastive learning framework. Specifically, we propose the Interleaved Cross Attention Networks (ICAN) as the representation model. The core module, Interleaved Cross Attention (ICA), is designed based on the observation that the temporal dependencies of interleaved down-sampled subsequences of telemetry features are highly similar to those of the original sequences, with minimal loss of critical information. Thus, ICAN captures the flow of information across interleaved time steps, effectively reducing computational complexity while enhancing the extraction of temporal dependencies in telemetry sequences. Furthermore, this paper presents a novel contrastive learning framework centered on ICAN. This framework incorporates a series of efficient modules, including methods for generating positive and negative sample pairs, an adaptive margin contrastive loss function, an anomaly criterion based on Exponentially Weighted Moving Average (EWMA), and an anomaly thresholding method based on Drift-aware Streaming Peaks Over Threshold (DSPOT). These techniques collectively improve the effectiveness of anomaly detection. Finally, experimental results on the AACS dataset, demonstrate the efficacy and superiority of the proposed method.
关键词
Anomaly detection, satellite telemetry data, contrastive learning, time series.
报告人
Haotian Zhao
Associate Researcher Harbin Institute of Technology

稿件作者
Haotian Zhao Harbin Institute of Technology
Ming Liu Harbin Institute of Technology
Lixian Zhang Harbin Institute of Technology
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重要日期

  • 会议日期

    11月21日

    2025

    至

    11月23日

    2025

  • 10月20日 2025

    初稿截稿日期

  • 11月23日 2025

    注册截止日期

主办单位
IEEE Instrumentation and Measurement Society
South China University of Technology
承办单位
South China University of Technology
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