基于噪声元学习的卫星遥测信号异常检测方法

doi:10.12305/j.issn.1001-506X.2025.02.02

Abstract

Abstract:

Due to the scarcity of prior knowledge in satellite telemetry data, conventional data-driven anomaly detection methods are difficult to accurately identify abnormal states, a satellite telemetry signal anomaly detection algorithm based on meta learning and dynamic scaling threshold method is proposed. Firstly, a set of initial parameters of a long short-term memory (LSTM) neural network with the ability to quickly adapt to small sample tasks is solved through meta learning algorithms. The noise is added to the network weights during the training process to further improve generalization performance of the model. Secondly, the dynamic scaling threshold method is used to analyze the prediction error sequence, define the abnormal threshold for dynamic changes, and mark the index of abnormal points to achieve anomaly detection of satellite telemetry data. Finally, the effectiveness of the proposed algorithm is verified through two sets of satellite telemetry signal examples. The simulation results show that the proposed method can improve the overfitting phenomenon of the prediction model and reduce the probability of missed alarms.

Key words: satellite telemetry signal, abnormal detection, longshort-term memory (LSTM) neural net-work, meta-learning

CLC Number:

V474

Pengfei GUO, Kai JIN, Qifeng CHEN, Caisheng WEI. Anomaly detection method of satellite telemetry signal based on noise meta learning[J]. Systems Engineering and Electronics, 2025, 47(2): 352-359.

Figures/Tables 9

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References 31

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模型参数	数值
序列长度	10
训练批次大小	8
隐含层大小	16
网络层数	4

预测算法	窗口阈值法			动态放缩阈值法
预测算法	p_a/%	p_r/%	F₁值	p_a/%	p_r/%	F₁值
DNN	53.8	63.6	0.582 9	58.8	60.6	0.596 9
LSTM	73.5	75.8	0.746 3	77.4	72.7	0.749 8
M-LSTM	76.3	87.9	0.816 9	82.9	84.8	0.838 4
M-LSTM-AN	83.8	93.9	0.885 6	93.8	90.9	0.923 3

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Anomaly detection method of satellite telemetry signal based on noise meta learning

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