2-D Predictive Filters for Polynomial Signals With Applications to Wind Proﬁler Data
Laakom, Firas (2019-10)
10 / 2019
Proceedings of XXXV Finnish URSI Convention on Radio Science
Julkaisun pysyvä osoite on
Polynomial predictors are known for their ability, in the absence of noise, to exactly predict a future value of a polynomial signal of a ﬁxed order. One-dimensional ﬁltering is a mature ﬁeld and sophisticated ﬁlter design methods have already been heavily studied. Real world 2-D and higher order datasets are widely available for a multitude of applications. Thus, it is interesting to extend the existing one-dimensional polynomial predictors, e.g. Heinonen-Neuvo ﬁlter, to higher dimensional spaces. In this paper, we propose a novel 2-D polynomial predictor and evaluate its performance on a newly generated wind speed dataset.
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