TUTCRIS - Tampereen teknillinen yliopisto

TUTCRIS

2-D Predictive Filters for Polynomial Signals With Applications to Wind Profiler Data

Tutkimustuotos

Standard

2-D Predictive Filters for Polynomial Signals With Applications to Wind Profiler Data. / Laakom, Firas.

Proceedings of XXXV Finnish URSI Convention on Radio Science. URSI, 2019.

Tutkimustuotos

Harvard

Laakom, F 2019, 2-D Predictive Filters for Polynomial Signals With Applications to Wind Profiler Data. julkaisussa Proceedings of XXXV Finnish URSI Convention on Radio Science. URSI, 1/01/00.

APA

Laakom, F. (2019). 2-D Predictive Filters for Polynomial Signals With Applications to Wind Profiler Data. teoksessa Proceedings of XXXV Finnish URSI Convention on Radio Science URSI.

Vancouver

Laakom F. 2-D Predictive Filters for Polynomial Signals With Applications to Wind Profiler Data. julkaisussa Proceedings of XXXV Finnish URSI Convention on Radio Science. URSI. 2019

Author

Laakom, Firas. / 2-D Predictive Filters for Polynomial Signals With Applications to Wind Profiler Data. Proceedings of XXXV Finnish URSI Convention on Radio Science. URSI, 2019.

Bibtex - Lataa

@inproceedings{6edc4a32194d4e49a95e3257351c34d8,
title = "2-D Predictive Filters for Polynomial Signals With Applications to Wind Profiler Data",
abstract = "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.",
author = "Firas Laakom",
year = "2019",
month = "10",
language = "English",
booktitle = "Proceedings of XXXV Finnish URSI Convention on Radio Science",
publisher = "URSI",

}

RIS (suitable for import to EndNote) - Lataa

TY - GEN

T1 - 2-D Predictive Filters for Polynomial Signals With Applications to Wind Profiler Data

AU - Laakom, Firas

PY - 2019/10

Y1 - 2019/10

N2 - 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.

AB - 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.

M3 - Conference contribution

BT - Proceedings of XXXV Finnish URSI Convention on Radio Science

PB - URSI

ER -