A Lightweight Satellite Orbit Anomaly Detection Method Based on Peak-AAE and Its Edge Deployment

Abstract

The detection of orbital anomalies is of critical importance to the safe operation of on-orbit spacecraft. Existing methods often rely on manually engineered features and are constrained by the limited computational resources of satellite equipment. This paper proposes a lightweight unsupervised anomaly detection method based on the Peak Adversarial Autoencoder (Peak-AAE). First, the Peak-AAE model is employed to reconstruct historical semi-major axis data, and the squared differences between the reconstructed and original data are taken as the residual sequence. The Automatic Multi-Scale Peak Detection (AMPD) algorithm is then applied to the residual sequence to identify anomalous points. In comparison with traditional approaches, this method eliminates the need for manual feature design. Moreover, the improved Peak-AAE model effectively reduces the false positive rate associated with the original AAE model, thereby enhancing detection accuracy. Finally, the compressed Peak-AAE model is deployed on edge devices for ground simulation verification. Experimental validation on the BEIDOU-3 G2 and IRNSS-1A satellites demonstrates that the Peak-AAE model achieves a minimum precision of 90.00% and a minimum recall of 84.74%.