{"title":"A comparison between filtering approach and spline approximation method in smoothing flight data","authors":"O. N. Korsun, Sekou Goro, M. H. Om","doi":"10.1007/s42401-023-00201-0","DOIUrl":null,"url":null,"abstract":"<div><p>Onboard measurement system, the function of which is to collect, record and process measurement information, performs these tasks by obtaining information from the sensors. However, the sensors are noisy, so it is necessary to combine multiple pieces of information to give a good accuracy to the signals provided by the onboard measurement system. The extended Kalman filter, which is one of the most widely used data fusion methods, is also one of the derivatives of the standard Kalman filter used for non-linear problems. On the other hand, the spline approximation, especially the cubic and Hermitian splines provide a very good and smooth estimate. It is known that the implementation of spline method is simpler than Kalman filtering. This paper describes an empirical comparative analysis of the extended Kalman filter and spline method. The impressive result is that a simple spline approximation in many cases performs better than a sophisticated Kalman filter.</p></div>","PeriodicalId":36309,"journal":{"name":"Aerospace Systems","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2023-02-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Aerospace Systems","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s42401-023-00201-0","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"Earth and Planetary Sciences","Score":null,"Total":0}
引用次数: 1
Abstract
Onboard measurement system, the function of which is to collect, record and process measurement information, performs these tasks by obtaining information from the sensors. However, the sensors are noisy, so it is necessary to combine multiple pieces of information to give a good accuracy to the signals provided by the onboard measurement system. The extended Kalman filter, which is one of the most widely used data fusion methods, is also one of the derivatives of the standard Kalman filter used for non-linear problems. On the other hand, the spline approximation, especially the cubic and Hermitian splines provide a very good and smooth estimate. It is known that the implementation of spline method is simpler than Kalman filtering. This paper describes an empirical comparative analysis of the extended Kalman filter and spline method. The impressive result is that a simple spline approximation in many cases performs better than a sophisticated Kalman filter.
期刊介绍:
Aerospace Systems provides an international, peer-reviewed forum which focuses on system-level research and development regarding aeronautics and astronautics. The journal emphasizes the unique role and increasing importance of informatics on aerospace. It fills a gap in current publishing coverage from outer space vehicles to atmospheric vehicles by highlighting interdisciplinary science, technology and engineering.
Potential topics include, but are not limited to:
Trans-space vehicle systems design and integration
Air vehicle systems
Space vehicle systems
Near-space vehicle systems
Aerospace robotics and unmanned system
Communication, navigation and surveillance
Aerodynamics and aircraft design
Dynamics and control
Aerospace propulsion
Avionics system
Opto-electronic system
Air traffic management
Earth observation
Deep space exploration
Bionic micro-aircraft/spacecraft
Intelligent sensing and Information fusion