Terminal height estimation using a Fading Gaussian Deterministic filter

In a recent work by the authors the concept of Fading Gaussian Deterministic filter was investigated. The algorithm is based on a set of equations derived from the minimization of a cost function where earlier data are progressively de-weighted by a fading factor. In such a way, the estimation was p...

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Vydáno v:Aerospace science and technology Ročník 55; s. 366 - 376
Hlavní autoři: de Angelis, Emanuele L., Ferrarese, Gastone, Giulietti, Fabrizio, Modenini, Dario, Tortora, Paolo
Médium: Journal Article
Jazyk:angličtina
Vydáno: Elsevier Masson SAS 01.08.2016
Témata:
Algorithms
Error analysis
Fading
Fading-memory filter
Gaussian
Height estimation
Mathematical models
Noise
Noise measurement
Self-tuning
Tuning
Height estimation
Fading-memory filter
Self-tuning
ISSN:1270-9638, 1626-3219
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Shrnutí:In a recent work by the authors the concept of Fading Gaussian Deterministic filter was investigated. The algorithm is based on a set of equations derived from the minimization of a cost function where earlier data are progressively de-weighted by a fading factor. In such a way, the estimation was proved to be less prone to problem unknowns. A tuning procedure was proposed that allows the resulting globally best estimator to evaluate the covariance of an effective measurement noise and the true estimation error, without any a-priori assumption. In the present paper, a general formulation is derived where the observed system is influenced by a control input. Also, a proof is derived for the proposed tuning criterion, which is shown to provide, under certain assumptions, the fading factor that best dampens the modeling errors with respect to measurement noise. The validity of the proposed approach is investigated by means of both numerical simulations and an experimental campaign, where height estimation is performed by fusing information from MEMS accelerometers and a barometric altimeter.
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ISSN:1270-9638
1626-3219
DOI:10.1016/j.ast.2016.06.013