# A Review of Anomaly Detection in Spacecraft Telemetry Data
## 1 Introduction  
Spacecraft [1, 2, 3, 4] are complex machines designed to carry out a variety of missions in outer space environments. Key types of spacecraft range from satellites to space probes, space stations, and landers, while applications include scientific research, exploration, communication, navigation, and military. The internal systems of these space machines are equipped with thousands of detailed telemetry channels to monitor parameters such as power, temperature, radiation, and instrumentations. Monitoring and diagnosing the status of telemetry data is one of the most import concerns for spacecraft safety. In fact, failure to detect or notify with regard to any abnormal behavior can result in irreparable loss and/or failure of the spacecraft during launch or while in space. Therefore, it is vital to efficiently and continually detect anomalies present in spacecraft telemetry to guarantee its reliable and safe operation during space missions. Spacecraft telemetry data are acquired from various subsystems and components at several temporal resolutions. These data are then transmitted to the ground telemetry station where the original variable information of each channel is restored. Telemetry data are then analyzed and interpreted by domain experts. Figure 1 summarizes the typical end-to-end communication between spacecraft, ground station, and domain experts. The complex nature of spacecraft telemetry data poses several challenges [5, 6] in developing appropriate anomaly detection methods:* High data volume: Spacecraft generate large time series telemetry data volumes with high dimensionality and complex relations between telemetry parameters, up to several terabytes of data per day. This high data volume poses significant challenges and difficulties for anomaly detection. * Complex multivariate data: Spacecraft telemetry data often include noisy and heterogeneous outputs produced by various system components. Some of them are discrete such as the switching status, while the others are continuous, e.g., temperature and radiation. These differences can reduce the performance of anomaly detection methods. * Lack of ground truth: Anomaly detection methods are hindered by the scarcity of labeled anomalies, as such ground truth is needed both for supervised learning approaches and also for performance evaluation. The absence of labeled anomalies for the validation can be solved by using semi-supervised or unsupervised methods. * Need for domain expertise: Expert knowledge of spacecraft subsystems and components is often required when analyzing telemetry data, in particular to label anomalies or analyze the performance of the anomaly detection methods. * Real-time processing capabilities: Given that it is impossible for domain experts to manually monitor thousands of telemetry channels to identify anomalies, automatic techniques which can detect such anomalies in real time are needed. In certain cases, it is desirable to deploy anomaly detection techniques on onboard computers to ensure that issues are detected as quickly as possible. In recent decades, there has been a growing interest in exploiting anomaly detection methods to monitor spacecraft telemetry data. Many prominent methods and strategies have been proposed for the purpose of identifying anomalies [8; 9; 10; 11]. Such methods allow for mitigating action to be taken before incidents spread. This review paper aims to document the state of anomaly detection in spacecraft telemetry data by exposing and discussing the existing anomaly detection techniques in the literature as well as their performance. Five main criteria [12] were used to evaluate information from the reviewed literature, which are the following:  
* Accuracy: Information was extracted from reliable and well-known sources (peer-reviewed journals, books, official websites, etc.) based on proven facts. * Authority: The authors of the selected papers are affiliated with a reputable university or organization in the subject field.