1 Ocak 2021 Dergi makalesi Açık Erişim

Bahadur, Berkay; Nohutcu, Metin

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  <dc:creator>Bahadur, Berkay</dc:creator>
  <dc:creator>Nohutcu, Metin</dc:creator>
  <dc:date>2021-01-01</dc:date>
  <dc:description>Although the emergence of new satellite systems offers considerable opportunities, the integration of Global Navigation Satellite System (GNSS) multi-constellation entails more complicated approaches, especially for stochastic modeling. This study proposes a filtering approach that combines robust Kalman filtering and variance component estimation to specify the weights of multi-GNSS observations in single-frequency positioning. In this approach, robust Kalman filter resists the impact of unexpected outliers by introducing the equivalent covariance matrix, while multi-GNSS observation variances are determined adaptively in each epoch by using variance component estimation. The study demonstrated that the proposed filtering approach determines the variances of multi-GNSS observations more rigorously as a result of the assessment of the observation residuals. The results also showed that the positioning accuracy of single-frequency multi-GNSS positioning that depends on the conventional weighting approaches is improved by 18.5% on average with the employment of the proposed filtering approach and its improvement ratio can exceed 30% in some stations.</dc:description>
  <dc:identifier>https://aperta.ulakbim.gov.trrecord/231342</dc:identifier>
  <dc:identifier>oai:aperta.ulakbim.gov.tr:231342</dc:identifier>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>http://www.opendefinition.org/licenses/cc-by</dc:rights>
  <dc:source>MEASUREMENT 173</dc:source>
  <dc:title>Integration of variance component estimation with robust Kalman filter for single-frequency multi-GNSS positioning</dc:title>
  <dc:type>info:eu-repo/semantics/article</dc:type>
  <dc:type>publication-article</dc:type>
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