A comparison between filtering approach and spline approximation method in smoothing flight data-Reference-Cited by-同舟云学术

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A comparison between filtering approach and spline approximation method in smoothing flight data

Published:2023-02-26 Issue:3 Volume:6 Page:473-480
ISSN:2523-3947
Container-title:Aerospace Systems
language:en
Short-container-title:AS

Author:

Korsun O. N.,Goro Sekou,Om M. H.

Funder

Russian Foundation for Fundamental Research

Publisher

Springer Science and Business Media LLC

Subject

Space and Planetary Science,Computers in Earth Sciences,Mechanical Engineering,Aerospace Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s42401-023-00201-0.pdf

Reference26 articles.

1. Schum DJ (2003) Noise reduction via signal processing: (1) strategies used in other industries. Hearing J 56(5):27–32. https://doi.org/10.1097/01.HJ.0000293885.26777.b5

2. St-Pierre M, Gingras D (2004) Comparison between the unscented Kalman filter and the extended Kalman filter for the position estimation module of an integrated navigation information system. IEEE Intell Veh Symp 2004:831–835. https://doi.org/10.1109/IVS.2004.1336492

3. de Marina HG, Pereda FJ, Giron-Sierra JM, Espinosa F (2012) UAV attitude estimation using extended Kalman filter and TRIAD. IEEE Trans Ind Electron 59(11):4465–4474. https://doi.org/10.1109/TIE.2011.2163913

4. Meinhold RJ, Singpurwalla ND (1983) Understanding the Kalman Filter. Am Stat 37(2):123–127. https://doi.org/10.1080/00031305.1983.10482723

5. Unser M (1999) Splines: a perfect fit for signal and image processing. IEEE Signal Process Mag 16(6):22–38. https://doi.org/10.1109/79.799930

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1. Algorithms for Selection and Combination of Sliding Intervals for Identification Based on Large Volumes of Flight Data;E3S Web of Conferences;2023

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