{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,23]],"date-time":"2026-06-23T22:49:27Z","timestamp":1782254967191,"version":"3.54.5"},"reference-count":40,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2019,2,13]],"date-time":"2019-02-13T00:00:00Z","timestamp":1550016000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["61571160,61803121,61701131"],"award-info":[{"award-number":["61571160,61803121,61701131"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Fault detection for sensors of unmanned aerial vehicles is essential for ensuring flight security, in which the flight control system conducts real-time control for the vehicles relying on the sensing information from sensors, and erroneous sensor data will lead to false flight control commands, causing undesirable consequences. However, because of the scarcity of faulty instances, it still remains a challenging issue for flight sensor fault detection. The one-class support vector machine approach is a favorable classifier without negative samples, however, it is sensitive to outliers that deviate from the center and lacks a mechanism for coping with them. The compactness of its decision boundary is influenced, leading to the degradation of detection rate. To deal with this issue, an optimized one-class support vector machine approach regulated by local density is proposed in this paper, which regulates the tolerance extents of its decision boundary to the outliers according to their extent of abnormality indicated by their local densities. The application scope of the local density theory is narrowed to keep the internal instances unchanged and a rule for assigning the outliers continuous density coefficients is raised. Simulation results on a real flight control system model have proved its effectiveness and superiority.<\/jats:p>","DOI":"10.3390\/s19040771","type":"journal-article","created":{"date-parts":[[2019,2,14]],"date-time":"2019-02-14T03:21:46Z","timestamp":1550114506000},"page":"771","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":56,"title":["UAV Sensor Fault Detection Using a Classifier without Negative Samples: A Local Density Regulated Optimization Algorithm"],"prefix":"10.3390","volume":"19","author":[{"given":"Kai","family":"Guo","sequence":"first","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Liansheng","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shuhui","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Datong","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiyuan","family":"Peng","sequence":"additional","affiliation":[{"name":"School of Electronics and Information Engineering, Harbin Institute of Technology, Harbin 150080, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2019,2,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"253","DOI":"10.1016\/j.microrel.2018.03.032","article-title":"A hybrid approach for UAV flight data estimation and prediction based on flight mode recognition","volume":"84","author":"Wang","year":"2018","journal-title":"Microelectron. Reliab."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.ifacol.2015.09.556","article-title":"IMU sensor fault diagnosis and estimation for quadrotor UAVs","volume":"48","author":"Avram","year":"2015","journal-title":"IFAC-PapersOnLine"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1007\/s10115-014-0754-y","article-title":"Online data-driven anomaly detection in autonomous robots","volume":"43","author":"Khalastchi","year":"2015","journal-title":"Knowl. Inf. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"4515828","DOI":"10.1155\/2018\/4515828","article-title":"Cooperative Virtual Sensor for Fault Detection and Identification in Multi-UAV Applications","volume":"2018","author":"Suarez","year":"2018","journal-title":"J. Sens."},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wang, L., Li, M., Liu, L., and Liu, D. (2018, January 15\u201317). Exhaust gas temperature sensing data anomaly detection for aircraft auxiliary power unit condition monitoring. Proceedings of the 2018 International Conference on Sensing, Diagnostics, Prognostics, and Control (SDPC), Xi\u2019an, China.","DOI":"10.1109\/SDPC.2018.8664831"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"3768","DOI":"10.1109\/TIE.2015.2417501","article-title":"A survey of fault diagnosis and fault-tolerant techniques Part I: Fault diagnosis","volume":"62","author":"Gao","year":"2015","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"272","DOI":"10.1016\/j.ifacol.2016.07.125","article-title":"An Actuator Fault Detection and Isolation method design for Planar Vertical Take-off and Landing Unmanned Aerial Vehicle modelled as a qLPV system","volume":"49","author":"Theilliol","year":"2016","journal-title":"IFAC-PapersOnLine"},{"key":"ref_8","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 Trans."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1007\/s10846-015-0295-y","article-title":"LPV Model-Based Tracking Control and Robust Sensor Fault Diagnosis for a Quadrotor UAV","volume":"84","author":"Ponsart","year":"2016","journal-title":"J. Intell. Robot. Syst. Theory Appl."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"40990","DOI":"10.1109\/ACCESS.2018.2854224","article-title":"An on-line state of health estimation of lithium-ion battery using unscented particle filter","volume":"6","author":"Liu","year":"2018","journal-title":"IEEE Access"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Liu, L., Peng, Y., and Liu, D. (2018). An Electro-Mechanical Actuator motor voltage estimation method with a feature-aided Kalman Filter. Sensors, 18.","DOI":"10.3390\/s18124190"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1050","DOI":"10.1016\/j.jclepro.2018.06.182","article-title":"On-line life cycle health assessment for lithium-ion battery in electric vehicles","volume":"199","author":"Liu","year":"2018","journal-title":"J. Clean. Prod."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"264","DOI":"10.1016\/j.microrel.2017.03.008","article-title":"Quantitative selection of sensor data based on improved permutation entropy for system remaining useful life prediction","volume":"75","author":"Liu","year":"2017","journal-title":"Microelectron. Reliab."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"170","DOI":"10.1016\/j.measurement.2018.02.044","article-title":"A novel approach for analog circuit fault diagnosis based on deep belief network","volume":"121","author":"Zhao","year":"2018","journal-title":"Measurement"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1016\/j.ymssp.2017.06.035","article-title":"Multi-bearing weak defect detection for wayside acoustic diagnosis based on a time-varying spatial filtering rearrangement","volume":"100","author":"Zhang","year":"2018","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"1539","DOI":"10.1109\/TIE.2017.2733438","article-title":"Machine Health Monitoring Using Local Feature-Based Gated Recurrent Unit Networks","volume":"65","author":"Zhao","year":"2018","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"250","DOI":"10.1016\/j.jsv.2015.01.052","article-title":"Smoothness index-guided Bayesian inference for determining joint posterior probability distributions of anti-symmetric real Laplace wavelet parameters for identification of different bearing faults","volume":"345","author":"Wang","year":"2015","journal-title":"J. Sound Vib."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1016\/j.patcog.2018.01.006","article-title":"Infinite Bayesian one-class support vector machine based on Dirichlet process mixture clustering","volume":"78","author":"Zhang","year":"2018","journal-title":"Pattern Recognit."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"1376","DOI":"10.1109\/TGRS.2015.2479299","article-title":"A low-rank and sparse matrix decomposition-based mahalanobis distance method for hyperspectral anomaly detection","volume":"54","author":"Zhang","year":"2016","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"101","DOI":"10.15837\/ijccc.2014.1.870","article-title":"Network Anomaly Detection Based on Multi-scale Dynamic Characteristics of Traffic","volume":"9","author":"Yuan","year":"2014","journal-title":"Int. J. Comput. Commun. Control"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"845","DOI":"10.1109\/TPAMI.2013.209","article-title":"Domain anomaly detection in machine perception: A system architecture and taxonomy","volume":"36","author":"Kittler","year":"2014","journal-title":"IEEE Trans. Pattern Anal. Mach. Intell."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"38","DOI":"10.1016\/j.jprocont.2015.04.014","article-title":"Nonlinear plant-wide process monitoring using MI-spectral clustering and Bayesian inference-based multiblock KPCA","volume":"32","author":"Jiang","year":"2015","journal-title":"J. Process Control"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Dorobantu, A., Ozdemir, A.A., Turkoglu, K., Freeman, P., Murch, A., Mettler, B., and Balas, G. (2011, January 8\u201311). Frequency Domain System Identification for a Small, Low-Cost, Fixed-Wing UAV. Proceedings of the AIAA Guidance, Navigation, and Control Conference, Portland, OR, USA.","DOI":"10.2514\/6.2011-6719"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"89","DOI":"10.1002\/rob.20226","article-title":"Supporting wilderness search and rescue using a camera-equipped mini UAV","volume":"25","author":"Goodrich","year":"2008","journal-title":"J. F. Robot."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"400","DOI":"10.1016\/j.isatra.2017.06.001","article-title":"A review and comparison of fault detection and diagnosis methods for squirrel-cage induction motors: State of the art","volume":"70","author":"Liu","year":"2017","journal-title":"ISA Trans."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ilonen, J., Paalanen, P., Kamarainen, J.-K., and K\u00e4lvi\u00e4inen, H. (2006, January 20\u201324). Gaussian mixture pdf in one-class classification: Computing and utilizing confidence values. Proceedings of the 18th International Conference on Pattern Recognition, Hong Kong, China.","DOI":"10.1109\/ICPR.2006.595"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"16453","DOI":"10.1021\/ie502344q","article-title":"Multisubspace principal component analysis with local outlier factor for multimode process monitoring","volume":"53","author":"Song","year":"2014","journal-title":"Ind. Eng. Chem. Res."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"4324","DOI":"10.1016\/j.eswa.2015.01.010","article-title":"Fault detection analysis using data mining techniques for a cluster of smart office buildings","volume":"42","author":"Capozzoli","year":"2015","journal-title":"Expert Syst. Appl."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1016\/j.neucom.2016.03.007","article-title":"Differential feature based hierarchical PCA fault detection method for dynamic fault","volume":"202","author":"Zhou","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1016\/j.neucom.2016.09.076","article-title":"Online fault detection methods for chillers combining extended kalman filter and recursive one-class SVM","volume":"228","author":"Yan","year":"2017","journal-title":"Neurocomputing"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Fu, C., Duan, R., Kircali, D., and Kayacan, E. (2016). Onboard robust visual tracking for UAVs using a reliable global-local object model. Sensors, 16.","DOI":"10.3390\/s16091406"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1109\/TITS.2007.908567","article-title":"Information-theoretic data registration for UAV-based sensing","volume":"9","author":"Jwa","year":"2008","journal-title":"IEEE Trans. Intell. Transp. Syst."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"15","DOI":"10.1016\/j.chemolab.2015.11.010","article-title":"Robust one-class SVM for fault detection","volume":"151","author":"Xiao","year":"2016","journal-title":"Chemom. Intell. Lab. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Amer, M., Goldstein, M., and Abdennadher, S. (2013, January 11). Enhancing one-class support vector machines for unsupervised anomaly detection. Proceedings of the ACM SIGKDD Workshop on Outlier Detection and Description, Chicago, IL, USA.","DOI":"10.1145\/2500853.2500857"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"121","DOI":"10.1016\/j.patcog.2016.03.028","article-title":"High-dimensional and large-scale anomaly detection using a linear one-class SVM with deep learning","volume":"58","author":"Erfani","year":"2016","journal-title":"Pattern Recognit."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"473","DOI":"10.1007\/s10846-017-0690-7","article-title":"A Novel Sensor Fault Detection in an Unmanned Quadrotor Based on Adaptive Neural Observer","volume":"90","author":"Aboutalebi","year":"2018","journal-title":"J. Intell. Robot. Syst."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Sun, R., Cheng, Q., Wang, G., and Ochieng, W.Y. (2017). A Novel Online Data-Driven Algorithm for Detecting UAV Navigation Sensor Faults. Sensors, 17.","DOI":"10.3390\/s17102243"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1061\/(ASCE)AS.1943-5525.0000579","article-title":"Active Fault-Tolerant Control of UAV Dynamics against Sensor-Actuator Failures","volume":"29","author":"Caliskan","year":"2016","journal-title":"J. Aerosp. Eng."},{"key":"ref_39","unstructured":"Paw, Y.C. (2009). Synthesis and validation of flight control for UAV. [Ph.D. Thesis, University of Minnesota]."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"1300","DOI":"10.1109\/TMECH.2013.2258678","article-title":"Model-Based and Data-Driven Fault Detection Performance for a Small UAV","volume":"18","author":"Freeman","year":"2013","journal-title":"IEEE Trans. Mechatron."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/771\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T12:31:51Z","timestamp":1760185911000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/19\/4\/771"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2019,2,13]]},"references-count":40,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2019,2]]}},"alternative-id":["s19040771"],"URL":"https:\/\/doi.org\/10.3390\/s19040771","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2019,2,13]]}}}