2-D Predictive Filters for Polynomial Signals With Applications to Wind Profiler Data
Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Professional
Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of XXXV Finnish URSI Convention on Radio Science |
| Publisher | URSI |
| Number of pages | 4 |
| Publication status | Published - Oct 2019 |
| Publication type | D3 Professional conference proceedings |
| Event | Finnish URSI Convention on Radio Science - Duration: 1 Jan 1900 → … |
Conference
| Conference | Finnish URSI Convention on Radio Science |
|---|---|
| Period | 1/01/00 → … |
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.