{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,25]],"date-time":"2026-06-25T14:56:49Z","timestamp":1782399409614,"version":"3.54.5"},"reference-count":31,"publisher":"MDPI AG","issue":"17","license":[{"start":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T00:00:00Z","timestamp":1661299200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["72071208"],"award-info":[{"award-number":["72071208"]}]},{"name":"National Natural Science Foundation of China","award":["2020RC4046"],"award-info":[{"award-number":["2020RC4046"]}]},{"name":"Science and Technology Innovation Program of Hunan Province","award":["72071208"],"award-info":[{"award-number":["72071208"]}]},{"name":"Science and Technology Innovation Program of Hunan Province","award":["2020RC4046"],"award-info":[{"award-number":["2020RC4046"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Anomaly detection based on telemetry data is a major issue in satellite health monitoring which can identify unusual or unexpected events, helping to avoid serious accidents and ensure the safety and reliability of operations. In recent years, sparse representation techniques have received an increasing amount of interest in anomaly detection, although its applications in satellites are still being explored. In this paper, a novel sparse feature-based anomaly detection method (SFAD) is proposed to identify hybrid anomalies in telemetry. First, a telemetry data dictionary and the corresponding sparse matrix are obtained through K-means Singular Value Decomposition (K-SVD) algorithms, then sparse features are defined from the sparse matrix containing the local dynamics and co-occurrence relations in the multivariate telemetry time series. Finally, lower-dimensional sparse features vectors are input to a one-class support vector machine (OCSVM) to detect anomalies in telemetry. Case analysis based on satellite antenna telemetry data shows that the detection precision, F1-score and FPR of the proposed method are improved compared with other existing multivariate anomaly detection methods, illustrating the good effectiveness of this method.<\/jats:p>","DOI":"10.3390\/s22176358","type":"journal-article","created":{"date-parts":[[2022,8,24]],"date-time":"2022-08-24T23:48:58Z","timestamp":1661384938000},"page":"6358","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["Anomaly Detection in Satellite Telemetry Data Using a Sparse Feature-Based Method"],"prefix":"10.3390","volume":"22","author":[{"given":"Jiahui","family":"He","sequence":"first","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Zhijun","family":"Cheng","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bo","family":"Guo","sequence":"additional","affiliation":[{"name":"College of Systems Engineering, National University of Defense Technology, Changsha 410073, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2022,8,24]]},"reference":[{"key":"ref_1","first-page":"6","article-title":"On-orbit fault statistical analysis for remote sensing satellite","volume":"3","author":"Zhang","year":"2015","journal-title":"Spacecr. 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