# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 3 State of the Art Anomaly Detection for Spacecraft Telemetry Data
### Artificial Intelligence Methods
#### 3.2.1 Machine Learning Methods  
Machine Learning (ML) has significant potential to enhance anomaly detection in spacecraft telemetry. ML approaches are used to build a model that can differentiate between normal and abnormal instances in telemetry data. In recent years, several ML approaches have been employed for anomaly detection in spacecraft telemetry systems. Each of these approaches possesses advantages and limitations. The choice of the selected approach depends on the type of anomaly and the availability of ground truth. ML-driven approaches for anomaly detection include Support Vector Machine (SVM) models, nearest neighbors approaches, clustering approaches, dimension reduction methods, and neural networks. Support Vector Machine (SVM): SVM is a supervised ML method used widely in the literature to identify abnormal behaviors of spacecraft. For instance, SVM [37] was used to detect anomalies in the control subsystem of an anonymized in-orbit satellite. It examined the behavior of 12 parameters (e.g., gyroscope, earth sensor, controller, etc. ), and it obtained an accuracy of 97.4%. The use of SVM was also investigated for the NASA SMAP/MSL dataset [38], obtaining a recall of 85.7% and a precision of 84.1%. Another work which utilized the SVM with ESA OPS-SAT telemetry data has been shown in [26]. In this work, SVM achieved 86.62% and 66.37% with respect to precision and recall, respectively. Different unsupervised variants of the SVM model have been used for anomaly detection on spacecraft telemetry such as One-Class SVM (OCSVM) [36; 39; 40], Least Squares SVM (LS-SVM) [28], and Integrated Least Squares SVM (ILS-SVM) [41]. Nearest neighbors: Anomaly detection of spacecraft telemetry has also been tackled with nearest neighbors approaches. Nearest neighbors [42] anomaly detection methods assume that normal data points arise in dense, clustered neighborhoods, while anomalies appear far from their nearest neighbors. These methods require a distance or similarity measure defined between two data instances. K-Nearest Neighbors (KNN) [10; 26; 36; 40] and improved KNN [43] are two methods which have been used to conduct anomaly detection on spacecraft telemetry data. The experimental study performed on the OPS-SAT telemetry dataset in [26] revealed that the KNN approach provides decent anomaly detection, reaching 61.54% in terms of recall, which is slightly lower than the results obtained by the SVM model. Improved KNN, which was tested on the ADAPT-Lite EPS (Electrical Power System) dataset, has obtained a higher accuracy of anomaly detection compared to the neural network method. However, the proposed method suffered from poor performance and is computationally heavy when faced with large and extremely uneven distribution sample datasets. Clustering approaches: Detecting unexpected patterns in the spacecraft telemetry data has also been attempted using clustering approaches [39; 44; 45; 46]. For example, [PERSON] et al. [44] developed a normal behavior clustering anomaly detection approach. They performed this method to detect anomalies of six parameters related to the power subsystem of an actual in-orbit satellite. [PERSON] et al. [45] adopted an extended dominant sets clustering algorithm to distinguish between normal and abnormal samples of synthetic and real telemetry datasets generated by the Tianping-2B satellite. The proposed clustering algorithm managed to increase the anomaly detection score by 3-10%. The works presented in [39; 46] have proposed different clustering strategies to detect anomalies. The first one uses probabilistic clustering and dimensionality reduction. It was implemented to catch faults in the telemetry data of the Small Demonstration Satellite 4 (SDS-4) of the Japan Aerospace Exploration Agency (JAXA). The second paper proposes a density peak clustering-based two-stage anomaly detection approach to check whether the system of the analogue solar sensor on the Zheda Pixing 1A (ZDPS-1A) satellite is normal or not.