Smoothing and Interpolating Noisy GPS Data with Smoothing Splines
This addresses data quality issues for GPS trajectory analysis in oceanography, but it is incremental as it applies existing smoothing spline methods to a specific domain with tailored parameter selection.
The paper tackled the problem of smoothing noisy, irregularly sampled GPS data with non-Gaussian noise and outliers, using smoothing splines with parameters chosen from physical reasoning, and demonstrated effectiveness on oceanographic drifter data.
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.