Deep Metric Learning for Abnormal Rotation Detection of Satellites from Irregularly Sampled Light Curve



Published Sep 4, 2023
Jun Yoshida Ryosuke Togawa Taichiro Sano


In recent years, satellites have become an indispensable infrastructure in our lives. The number of satellites is increasing yearly and becoming increasingly active. To use satellites safely, it is crucial to manage them and detect the anomaly of satellites as much as possible. However, it currently takes skilled operators to detect an anomaly, and it is difficult for even skilled operators to detect the anomaly early without the telemetry data in cases such as an abnormal rotation. To address these challenges, we tested the feasibility of using deep metric learning for early anomaly detection from the irregularly sampled light curve. One of the characteristics of a light curve is unequally spaced because the optical sensor on the ground can only observe the subject at night and not when the weather is terrible. Given an irregularly sampled light curve, our model employs a long short-term memory (LSTM) unit of encoding the temporal dynamics and learns the embedding on the feature space using triplet loss. Then, an anomaly score is calculated based on pairwise distances between segments from the learned embedding in the feature space. With actual data from the satellite being operated, we showed the effectiveness of our model and the feasibility of early anomaly detection. Also, by exploring learned embedding in the feature space, we show that our model could capture the continuous state of the satellite.  

Abstract 13 | PDF Downloads 21



anomaly detection, satellites, deep learning, metric learning, lightcurve

Dongjin Song, Ning Xia, Wei Cheng, Haifeng Chen, Dacheng Tao (2018). Deep r-th Root of Rank Supervised Joint Binary Embedding for Multivariate Time Series Retrieval. KDD 2018.

K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio. (2014). Learning phrase representations using RNN encoder- decoder for statistical machine translation. arXiv:1406.1078 .

Sepp H. and Jürgen S. (1997). Long short-term memory. Neural Computation 9, 8 , 1735–1780.1

Gregory P Badura, Christopher R Valenta, and Brian Gunte r (2022). Convolutional neural networks for inference of space object attitude status. The Journal of the Astronautical Sciences, pages 1–34, 2022.

Gregory P. Badura, Christopher R. Valenta, Layne Churchill, Douglas A. Hope (2022). Recurrent Neural Network Autoencoders for Spin Stability Classification of Irregularly Sampled Light Curves. (AMOS) –

Phan Dao, Kristen Haynes, Stephen Gregory, Jeffrey Hollon, Tamara Payne, and Kimberly Kinateder (2019). Machine classification and sub-classification pipeline for geo light curves. In Proceedings of the Advanced Maui Optical and Space Surveillance Technologies Conference, 2019.

Sara Abdelghafar, Ashraf Darwish, Aboul Ella Hassanien, Mohamed Yahia, Afaf Zaghrout (2019). Anomaly detection of satellite telemetry based on optimized extreme learning machine. Journal of Space Safety Engineering December 2019, Pages 291-298

Schroff, Kalenichenko, and Philbin (2015). “FaceNet: A Unified Embedding for Face Recognition and Clustering.” CVPR 2015.

Van der Maaten, Laurens, and Geoffrey Hinton (2008). "Visualizing data using t-SNE." Journal of machine learning research 9.11.
Special Session Papers