Integration of variance component estimation with robust Kalman filter for single-frequency multi-GNSS positioning


Bahadur B., Nohutcu M.

MEASUREMENT, vol.173, 2021 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 173
  • Publication Date: 2021
  • Doi Number: 10.1016/j.measurement.2020.108596
  • Journal Name: MEASUREMENT
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, INSPEC
  • Keywords: Single-frequency positioning, Robust Kalman filter, Variance component estimation, Multi-GNSS, WEIGHTING APPROACH, CORRECTION MODEL, LOW-COST, GPS, TIME, PERFORMANCE, DISCRETE, SOFTWARE, BEIDOU, BIASES
  • Hacettepe University Affiliated: Yes

Abstract

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.