{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:44:50Z","timestamp":1760179490036,"version":"build-2065373602"},"reference-count":61,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2020,10,24]],"date-time":"2020-10-24T00:00:00Z","timestamp":1603497600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003524","name":"Ministry of Business, Innovation and Employment","doi-asserted-by":"publisher","award":["UOOX-1208"],"award-info":[{"award-number":["UOOX-1208"]}],"id":[{"id":"10.13039\/501100003524","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>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\u2014using Akaike\u2019s Information Criterion\u2014favours alternative models, such as an Ornstein\u2013Uhlenbeck 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).<\/jats:p>","DOI":"10.3390\/s20216050","type":"journal-article","created":{"date-parts":[[2020,10,26]],"date-time":"2020-10-26T03:51:47Z","timestamp":1603684307000},"page":"6050","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Comparison of Enhanced Noise Model Performance Based on Analysis of Civilian GPS Data"],"prefix":"10.3390","volume":"20","author":[{"given":"Andy W. R.","family":"Soundy","sequence":"first","affiliation":[{"name":"Department of Physics, University of Otago, 730 Cumberland St, Dunedin 9016, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bradley J.","family":"Panckhurst","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Otago, 730 Cumberland St, Dunedin 9016, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Phillip","family":"Brown","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Otago, 730 Cumberland St, Dunedin 9016, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Andrew","family":"Martin","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Otago, 730 Cumberland St, Dunedin 9016, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2022-0380","authenticated-orcid":false,"given":"Timothy C. A.","family":"Molteno","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Otago, 730 Cumberland St, Dunedin 9016, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4797-1712","authenticated-orcid":false,"given":"Daniel","family":"Schumayer","sequence":"additional","affiliation":[{"name":"Department of Physics, University of Otago, 730 Cumberland St, Dunedin 9016, New Zealand"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,10,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5005","DOI":"10.1029\/96JB03860","article-title":"Precise point positioning for the efficient and robust analysis of GPS data from large networks","volume":"102","author":"Zumberge","year":"1997","journal-title":"J. Geophys. 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