{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:35:33Z","timestamp":1760236533484,"version":"build-2065373602"},"reference-count":39,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2021,11,30]],"date-time":"2021-11-30T00:00:00Z","timestamp":1638230400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"the National Science and Technology Major Project of China","award":["2019ZX04026001"],"award-info":[{"award-number":["2019ZX04026001"]}]},{"DOI":"10.13039\/501100019082","name":"Shanghai Aerospace Science and Technology Innovation Fund","doi-asserted-by":"publisher","award":["SAST52016001"],"award-info":[{"award-number":["SAST52016001"]}],"id":[{"id":"10.13039\/501100019082","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>An inertial platform is the key component of a remote sensing system. During service, the performance of the inertial platform appears in degradation and accuracy reduction. For better maintenance, the inertial platform system is checked and maintained regularly. The performance change of an inertial platform can be evaluated by detection data. Due to limitations of detection conditions, inertial platform detection data belongs to small sample data. In this paper, in order to predict the performance of an inertial platform, a prediction model for an inertial platform is designed combining a sliding window, grey theory and neural network (SGMNN). The experiments results show that the SGMNN model performs best in predicting the inertial platform drift rate compared with other prediction models.<\/jats:p>","DOI":"10.3390\/rs13234864","type":"journal-article","created":{"date-parts":[[2021,12,1]],"date-time":"2021-12-01T01:45:02Z","timestamp":1638323102000},"page":"4864","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["A Performance Prediction Method Based on Sliding Window Grey Neural Network for Inertial Platform"],"prefix":"10.3390","volume":"13","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7554-2635","authenticated-orcid":false,"given":"Langfu","family":"Cui","sequence":"first","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China"}]},{"given":"Qingzhen","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China"}]},{"given":"Liman","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2224-2337","authenticated-orcid":false,"given":"Chenggang","family":"Bai","sequence":"additional","affiliation":[{"name":"School of Automation Science and Electrical Engineering, Beijing University of Aeronautics and Astronautics, Beijing 100191, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,30]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1620","DOI":"10.1177\/0954410017699009","article-title":"Robust finite time second order sliding mode stabilization control for floated inertial platform","volume":"232","author":"Yu","year":"2018","journal-title":"Proc. Ins. Mech. Eng."},{"key":"ref_2","first-page":"612","article-title":"Initial Self-Alignment Method for Inertial Platform on a Stationary Base","volume":"38","author":"Ding","year":"2017","journal-title":"J. Astron."},{"key":"ref_3","first-page":"61","article-title":"Contrast Analysis of High-accuracy Methods for Stabilization Loop of Inertial Navigation Platform","volume":"3","author":"Li","year":"2018","journal-title":"Missile Space Veh."},{"key":"ref_4","first-page":"71","article-title":"Research on Calibration Method of Installation Error of Star Sensor in Inertial Platform","volume":"16","author":"Chen","year":"2017","journal-title":"Navig. Control"},{"key":"ref_5","first-page":"941","article-title":"Remaining Useful Lifetime Prediction Method of Controlled Systems Considering Performance Degradation of Actuator","volume":"45","author":"Shi","year":"2019","journal-title":"Acta Autom. Sin."},{"key":"ref_6","first-page":"1316","article-title":"Research on the Digital Platform for Hybrid Inertial Navigation System","volume":"39","author":"Wang","year":"2018","journal-title":"Acta Armamen"},{"key":"ref_7","first-page":"285","article-title":"Research on reliability of inertial navigation system based on fuzzy GO methodology","volume":"58","author":"Wang","year":"2018","journal-title":"J. Dalian Univ. Technol."},{"key":"ref_8","first-page":"90","article-title":"Inertial Platform System Storage Reliability Prediction Method Based on Small Sample Data","volume":"33","author":"Yang","year":"2015","journal-title":"Aerosp. Control"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"737146","DOI":"10.1155\/2013\/737146","article-title":"Positioning Errors Predicting Method of Strapdown Inertial Navigation Systems Based on PSO-SVM","volume":"2013","author":"Yin","year":"2013","journal-title":"Abstr. Appl. Anal."},{"key":"ref_10","unstructured":"Zhang, Y. (2015). Research on Performance Evaluation Method of Inertial Navigation System Based on PSO-SVM, Harbin Institute of Technology."},{"key":"ref_11","first-page":"9","article-title":"Practical combination forecasting evaluation method for PINS comprehensive performance","volume":"22","author":"Dang","year":"2014","journal-title":"J. Chin. Inert. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"4463","DOI":"10.3390\/en13174463","article-title":"Road Tests of the Positioning Accuracy of INS\/GNSS Systems Based on MEMS Technology for Navigating Railway Vehicles","volume":"13","author":"Mariusz","year":"2020","journal-title":"Energies"},{"key":"ref_13","first-page":"9","article-title":"Modeling of MEMS gyroscope random errors based on grey model and RBF neural network","volume":"5","author":"Sun","year":"2017","journal-title":"J. Navig. Position."},{"key":"ref_14","first-page":"1","article-title":"A novel method based on a high-dynamic hybrid forecasting model for fiber optic gyroscope drift","volume":"29","author":"Cai","year":"2017","journal-title":"Sens. Mater."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"105","DOI":"10.1016\/j.knosys.2017.07.022","article-title":"Parameter auto-selection for hemispherical resonator gyroscope\u2019s long-term prediction model based on cooperative game theory","volume":"134","author":"Dai","year":"2017","journal-title":"Knowl.-Based Syst."},{"key":"ref_16","first-page":"111","article-title":"Adaptive prediction of remaining useful life for stochastic deteriorating equipment based on linear FBM process","volume":"47","author":"Gao","year":"2021","journal-title":"China Meas. Test"},{"key":"ref_17","unstructured":"Chen, D. (2017). Studies on SINS Calibration Performace Quantitative Evaluation Approach Based on Bayesian Smoothing, Harbin Engineering University."},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Pavlenko, I., Saga, M., Kuric, I., Kotliar, A., Basova, Y., Trojanowska, J., and Ivanov, V. (2020). Parameter Identification of Cutting Forces in Crankshaft Grinding Using Artificial Neural Networks. Materials, 13.","DOI":"10.3390\/ma13235357"},{"key":"ref_19","first-page":"1729881420908076","article-title":"Path planning optimization of six-degree-of-freedom robotic manipulators using evolutionary algorithms","volume":"17","author":"Lorencin","year":"2020","journal-title":"Int. J. Adv. Robot. Syst."},{"key":"ref_20","first-page":"189","article-title":"Adaptive remaining useful life prediction for inertial platform with uncertain measurements","volume":"35","author":"Li","year":"2014","journal-title":"J. Chang. Univ. Technol."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"5850","DOI":"10.1109\/TPEL.2019.2952620","article-title":"Remaining Useful Life Prediction of Battery Using a Novel Indicator and Framework with Fractional Grey Model and Unscented Particle Filter","volume":"35","author":"Chen","year":"2019","journal-title":"IEEE Trans. Power Electron."},{"key":"ref_22","first-page":"15","article-title":"Using Improved Non-linear Multivariate Grey Bernoulli Model to Evaluate China\u2019s CO2 Emission","volume":"32","author":"Pang","year":"2020","journal-title":"J. Grey Syst."},{"key":"ref_23","first-page":"1","article-title":"Remaining Useful Life Prognosis Based on Ensemble Long Short-Term Memory Neural Network","volume":"70","author":"Cheng","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1109\/TNNLS.2020.2977132","article-title":"A Neural Network-Based Joint Prognostic Model for Data Fusion and Remaining Useful Life Prediction","volume":"32","author":"Gao","year":"2021","journal-title":"IEEE Trans. Neural Netw. Learn. Syst."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2284","DOI":"10.1109\/TCYB.2019.2935066","article-title":"Online and Unsupervised Anomaly Detection for Streaming Data Using an Array of Sliding Windows and PDDs","volume":"51","author":"Zhang","year":"2021","journal-title":"IEEE Trans. Cybern."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/TIM.2021.3051996","article-title":"A Sliding Window Common Spatial Pattern for Enhancing Motor Imagery Classification in EEG-BCI","volume":"70","author":"Gaur","year":"2021","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40623-020-01219-w","article-title":"Performance estimate of some prototypes of inertial platform and strapdown marine gravimeters","volume":"72","author":"Yuan","year":"2020","journal-title":"Earth Planets Space"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Itu, C., Bratu, P., Borza1, P.N., Vlase1, S., and Lixandroiu, D. (2020). Design and Analysis of Inertial Platform Insulation of the ELI-NP Project of Laser and Gamma Beam Systems. Symmetry, 12.","DOI":"10.3390\/sym12121972"},{"key":"ref_29","unstructured":"Wang, M. (2020). Research on Continuous Tumbling Self-Calibration for Inertial Navigation Platform System, Harbin Institute of Technology."},{"key":"ref_30","first-page":"91","article-title":"The Research and Development of Grey Neural Network","volume":"31","author":"Yuan","year":"2009","journal-title":"J. Wuhan Univ. Technol."},{"key":"ref_31","first-page":"1532","article-title":"Recursive soluction to unbiased grey model and its optimization","volume":"31","author":"Shi","year":"2011","journal-title":"Syst. Eng.-Theory Pract."},{"key":"ref_32","first-page":"1094","article-title":"A Direct Method of the Unbiased GM(1,1)","volume":"25","author":"Mu","year":"2003","journal-title":"Syst. Eng. Electron."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1550147720967894","DOI":"10.1177\/1550147720967894","article-title":"A tags\u2019 arrival rate estimation method using weighted grey model(1,1) and sliding window in mobile radio frequency identification systems","volume":"16","author":"Zhang","year":"2020","journal-title":"Int. J. Distrib. Sens. Netw."},{"key":"ref_34","first-page":"15","article-title":"Prediction of Foundation Settlement Prediction Based on Improved Grey Markov Model","volume":"40","author":"Yang","year":"2017","journal-title":"Geomat. Spat. Inf. Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"2494","DOI":"10.1080\/00949655.2017.1299151","article-title":"A sliding window-based multi-stage clustering and probabilistic forecasting approach for large multivariate time series data","volume":"87","author":"Ren","year":"2017","journal-title":"J. Stat. Comput. Simul."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"1687814020919241","DOI":"10.1177\/1687814020919241","article-title":"Prediction and analysis of bearing vibration signal with a novel gray combination model","volume":"12","author":"Yuan","year":"2020","journal-title":"Adv. Mech. Eng."},{"key":"ref_37","first-page":"71","article-title":"Particle swarm adaptive satellite clock error prediction model based on grey theory","volume":"50","author":"Li","year":"2018","journal-title":"J. Harbin Inst. Technol."},{"key":"ref_38","first-page":"361","article-title":"Combination model of BP network and WPGM (1,1) for land subsidence prediction","volume":"36","author":"Yan","year":"2013","journal-title":"J. Hefei Univ. Technol."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.eswa.2015.09.052","article-title":"An improved grey neural network model for predicting transportation disruptions","volume":"45","author":"Liu","year":"2016","journal-title":"Expert Syst. Appl."}],"container-title":["Remote Sensing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4864\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:37:55Z","timestamp":1760168275000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2072-4292\/13\/23\/4864"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,30]]},"references-count":39,"journal-issue":{"issue":"23","published-online":{"date-parts":[[2021,12]]}},"alternative-id":["rs13234864"],"URL":"https:\/\/doi.org\/10.3390\/rs13234864","relation":{},"ISSN":["2072-4292"],"issn-type":[{"type":"electronic","value":"2072-4292"}],"subject":[],"published":{"date-parts":[[2021,11,30]]}}}