Functional principal component analysis of aircraft trajectories

Florence Nicol 1, *
* Corresponding author
1 MAIAA-PROBA - Equipe MAIAA-PROBA
MAIAA - ENAC - Laboratoire de Mathématiques Appliquées, Informatique et Automatique pour l'Aérien
Abstract : In Functional Data Analysis (FDA), the underlying structure of a raw observation is functional and data are assumed to be sample paths from a single stochastic process. Functional Principal Component Analysis (FPCA) generalizes the standard multivariate Principal Component Analysis (PCA) to the infinite-dimensional case by analyzing the covariance structure of functional data. By approximating infinite-dimensional random functions by a finite number of random score vectors, FPCA appears as a dimension reduction technique just as in the multivariate case and cuts down the complexity of data. This method is applied to aircraft trajectories and the problem of registration is discussed when phase and amplitude variations are mixed.
Document type :
Conference papers
Complete list of metadatas

Cited literature [18 references]  Display  Hide  Download

https://hal-enac.archives-ouvertes.fr/hal-00867957
Contributor : Laurence Porte <>
Submitted on : Monday, September 30, 2013 - 5:31:29 PM
Last modification on : Wednesday, July 24, 2019 - 11:49:25 PM
Long-term archiving on : Tuesday, December 31, 2013 - 4:27:52 AM

File

isiatm2013_submission_102.pdf
Files produced by the author(s)

Identifiers

  • HAL Id : hal-00867957, version 1

Collections

Citation

Florence Nicol. Functional principal component analysis of aircraft trajectories. ISIATM 2013, 2nd International Conference on Interdisciplinary Science for Innovative Air Traffic Management, Jul 2013, Toulouse, France. ⟨hal-00867957⟩

Share

Metrics

Record views

341

Files downloads

1546