Smoothing and Interpolating Noisy GPS Data with Smoothing Splines
Methodology
2020-02-18 v2 Data Analysis, Statistics and Probability
Applications
Computation
Machine Learning
Abstract
A comprehensive methodology is provided for smoothing noisy, irregularly sampled data with non-Gaussian noise using smoothing splines. We demonstrate how the spline order and tension parameter can be chosen a priori from physical reasoning. We also show how to allow for non-Gaussian noise and outliers which are typical in GPS signals. We demonstrate the effectiveness of our methods on GPS trajectory data obtained from oceanographic floating instruments known as drifters.
Keywords
Cite
@article{arxiv.1904.12064,
title = {Smoothing and Interpolating Noisy GPS Data with Smoothing Splines},
author = {Jeffrey J. Early and Adam M. Sykulski},
journal= {arXiv preprint arXiv:1904.12064},
year = {2020}
}
Comments
16 pages, 8 figures