Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data
| Autor: | Daniel Schumayer, Bradley J. Panckhurst, Phillip Brown, Andy W. R. Soundy, T. C. A. Molteno, A. D. Martin |
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| Rok vydání: | 2020 |
| Předmět: |
010504 meteorology & atmospheric sciences
Computer science 02 engineering and technology Error analysis for the Global Positioning System 01 natural sciences Biochemistry stochastic process Article Analytical Chemistry Ornstein–Uhlenbeck process 0202 electrical engineering electronic engineering information engineering GPS error Electrical and Electronic Engineering Instrumentation 0105 earth and related environmental sciences sensor fusion business.industry Autocorrelation 020206 networking & telecommunications Kalman filter White noise Sensor fusion Atomic and Molecular Physics and Optics noise models Noise embedded computing Autoregressive model Global Positioning System Akaike information criterion business Algorithm |
| Zdroj: | Sensors (Basel, Switzerland) Sensors Volume 20 Issue 21 |
| ISSN: | 1424-8220 |
| Popis: | 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&mdash using Akaike&rsquo s Information Criterion&mdash favours alternative models, such as an Ornstein&ndash 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). |
| Databáze: | OpenAIRE |
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