Sensors, Vol. 20, Pages 6050: Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data

Sensors, Vol. 20, Pages 6050: Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
Sensors doi: 10.3390/s20216050
Authors:
Andy W. R. Soundy
Bradley J. Panckhurst
Phillip Brown
Andrew Martin
Timothy C. A. Molteno
Daniel Schumayer

We recorded the time series of location data from stationary, single-frequency (L1) GPS positioning systems at a variety of geographic locations. The empirical autocorrelation function of these data shows significant temporal correlations. The Gaussian white noise model, widely used in sensor-fusion algorithms, does not account for the observed autocorrelations and has an artificially large variance. Noise-model analysis—using Akaike’s Information Criterion—favours alternative models, such as an Ornstein–Uhlenbeck or an autoregressive process. We suggest that incorporating a suitable enhanced noise model into applications (e.g., Kalman Filters) that rely on GPS position estimates will improve performance. This provides an alternative to explicitly modelling possible sources of correlation (e.g., multipath, shadowing, or other second-order physical phenomena).

Source: Sensors - October 24, 2020 Category: Biotechnology Authors: Andy W. R. Soundy Bradley J. Panckhurst Phillip Brown Andrew Martin Timothy C. A. Molteno Daniel Schumayer Tags: Article Source Type: research