Variance Reduction of Sequential Monte Carlo Approach for GNSS Phase Bias Estimation

Yumiao Tian, Maorong Ge and Frank Neitzel
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Yumiao Tian: Faculty of Geosciences and Environmental Engineering, Southwest Jiaotong University, Chengdu 611756, China
Maorong Ge: Institute of Geodesy and Geoinformation Science, Technische Universität Berlin, 10623 Berlin, Germany
Frank Neitzel: Institute of Geodesy and Geoinformation Science, Technische Universität Berlin, 10623 Berlin, Germany

Mathematics, 2020, vol. 8, issue 4, 1-15

Abstract: Global navigation satellite systems (GNSS) are an important tool for positioning, navigation, and timing (PNT) services. The fast and high-precision GNSS data processing relies on reliable integer ambiguity fixing, whose performance depends on phase bias estimation. However, the mathematic model of GNSS phase bias estimation encounters the rank-deficiency problem, making bias estimation a difficult task. Combining the Monte-Carlo-based methods and GNSS data processing procedure can overcome the problem and provide fast-converging bias estimates. The variance reduction of the estimation algorithm has the potential to improve the accuracy of the estimates and is meaningful for precise and efficient PNT services. In this paper, firstly, we present the difficulty in phase bias estimation and introduce the sequential quasi-Monte Carlo (SQMC) method, then develop the SQMC-based GNSS phase bias estimation algorithm, and investigate the effects of the low-discrepancy sequence on variance reduction. Experiments with practical data show that the low-discrepancy sequence in the algorithm can significantly reduce the standard deviation of the estimates and shorten the convergence time of the filtering.

Keywords: GNSS phase bias; sequential quasi-Monte Carlo; variance reduction (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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