Adversarial Network Auto-Encoder for Satellite Anomaly Detection
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Solution Overview
Problem
Current satellite anomaly detection methods suffer from low accuracy in automatic detection due to high error rates and inability to effectively handle high-dimensional telemetry data with uneven density distributions.
Innovation Solution
A satellite anomaly detection method utilizing an adversarial network auto-encoder, which combines a variational auto-encoder and a generative adversarial network to encode and decode telemetry data, determining the reconstruction error using Mahalanobis distance and wavelet variance for error threshold analysis, thereby distinguishing between normal and abnormal operating states.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional statistical-based anomaly detection methods are used, then the detection process is simple, but the detection accuracy is low especially for high-dimensional telemetry data with uneven density distributions
Solution Approach 1:
The patent introduces an adversarial network as an intermediary component between the input data and the anomaly detection mechanism. The adversarial network learns the underlying distribution of normal telemetry data and generates adversarial examples that challenge the anomaly detection system, thereby improving detection accuracy for high-dimensional data with uneven density distributions without requiring complex manual feature engineering
Solution Approach 2:
The patent transforms the anomaly detection problem by changing the parameter space from direct telemetry data analysis to latent space representation learned by the adversarial network. This parameter transformation enables the system to capture complex patterns in high-dimensional data while maintaining computational efficiency
2Measurement precision
If kernel principal component analysis (KPCA) is used to construct feature matrices, then association rules can be mined, but the method is not effective for high-dimensional telemetry parameters
Solution Approach 1:
The patent addresses the limitation of KPCA in handling high-dimensional data by introducing a new dimensional transformation approach through the adversarial network. The network learns a nonlinear mapping from the original high-dimensional telemetry space to a latent representation space, capturing complex relationships that traditional dimensionality reduction methods miss
3Extent of automation
If local linear mapping algorithm is used to reduce dimension and extract features, then detection can be performed, but automatic detection cannot be implemented and accuracy cannot be guaranteed
Solution Approach 1:
The patent enables automatic anomaly detection by making the system self-learning through the adversarial network. The network automatically learns the normal data distribution and anomaly patterns from telemetry data without requiring manual intervention or expert knowledge, while achieving high detection accuracy through the adversarial training mechanism
Data Source
AI summary
The present disclosure provides a satellite anomaly detection method and system for an adversarial network auto-encoder. The method includes: obtaining a variational auto-encoder and a generative adversarial network; adding the generative adversarial network to the variational auto-encoder, and determining an optimized variational auto-encoder; obtaining to-be-detected satellite telemetry data; and determining a current operating status of a satellite based on the to-be-detected satellite telemetry data by using the optimized variational auto-encoder, where the current operating status includes a normal operating state or an abnormal operating state. The satellite anomaly detection method and system for an adversarial network according to the present disclosure solve the low accuracy problem of automatic satellite anomaly detection in the prior art.