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However, the accurate modeling and analysis of flight data is challenging due to the influence of random noise. Meanwhile, existing methods are often inadequate in parameter selection and feature extraction when dealing with large-scale and high-dimensional flight data. This paper proposes a data-driven multivariate regression-based framework considering spatio-temporal correlation for UAV flight data anomaly detection and recovery, which integrates the techniques of correlation analysis (CA), one-dimensional convolutional neural network and long short-term memory (1D CNN-LSTM), and error filtering (EF), named CA-1DCL-EF. Specifically, CA is first performed on original UAV flight data to select parameters with correlation to reduce the model input and avoid the negative impact of irrelevant parameters on the model. Next, a regression model based on 1D CNN-LSTM is designed to fully extract the spatio-temporal features of UAV flight data and realize parameter mapping. Then, to overcome the effect of random noise, a filtering technique is introduced to smooth the errors to improve the anomaly detection performance. Finally, two common anomaly types are injected into real UAV flight datasets to verify the effectiveness of the proposed method.<\/jats:p>","DOI":"10.1093\/jcde\/qwae023","type":"journal-article","created":{"date-parts":[[2024,3,13]],"date-time":"2024-03-13T00:35:36Z","timestamp":1710290136000},"page":"176-193","source":"Crossref","is-referenced-by-count":16,"title":["Data-driven multivariate regression-based anomaly detection and recovery of unmanned aerial vehicle flight data"],"prefix":"10.1093","volume":"11","author":[{"given":"Lei","family":"Yang","sequence":"first","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University , Guiyang 550025 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shaobo","family":"Li","sequence":"additional","affiliation":[{"name":"School of Mechanical Engineering, Guizhou University , Guiyang 550025 , China"},{"name":"State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Chuanjiang","family":"Li","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Public Big Data, Guizhou University , Guiyang 550025 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Caichao","family":"Zhu","sequence":"additional","affiliation":[{"name":"State Key Laboratory of Mechanical Transmission, Chongqing University , Chongqing 400044 , China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2024,3,12]]},"reference":[{"key":"2024040913452287200_bib1","doi-asserted-by":"crossref","first-page":"317","DOI":"10.1016\/j.isatra.2016.11.005","article-title":"Neural adaptive observer-based sensor and actuator fault detection in nonlinear systems: Application in UAV","volume":"67","author":"Abbaspour","year":"2017","journal-title":"ISA Transactions"},{"key":"2024040913452287200_bib2","doi-asserted-by":"crossref","first-page":"355","DOI":"10.1016\/j.isatra.2022.01.014","article-title":"Intelligent framework for automated failure prediction, detection, and classification of mission critical autonomous flights","volume":"129","author":"Ahmad","year":"2022","journal-title":"ISA Transactions"},{"key":"2024040913452287200_bib3","first-page":"721","article-title":"A novel technique to assess UAV behavior using PCA-based anomaly detection algorithm","volume":"9","author":"Alos","year":"2020","journal-title":"International Journal of Mechanical Engineering and Robotics Research"},{"key":"2024040913452287200_bib4","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1134\/S2075108720010046","article-title":"Detecting contextual faults in unmanned aerial vehicles using dynamic linear regression and k-nearest neighbour classifier","volume":"11","author":"Alos","year":"2020","journal-title":"Gyroscopy and Navigation"},{"key":"2024040913452287200_bib5","first-page":"305","article-title":"UAV anomaly detection with distributed artificial intelligence based on LSTM-AE and AE","volume-title":"Advanced multimedia and ubiquitous engineering. 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