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Main Title: Variance Reduction of Sequential Monte Carlo Approach for GNSS Phase Bias Estimation
Author(s): Tian, Yumiao
Ge, Maorong
Neitzel, Frank
Type: Article
Language Code: en
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.
Issue Date: 3-Apr-2020
Date Available: 11-Jun-2020
DDC Class: 510 Mathematik
Subject(s): GNSS phase bias
sequential quasi-Monte Carlo
variance reduction
Journal Title: Mathematics
Publisher: MDPI
Publisher Place: Basel
Volume: 8
Issue: 4
Article Number: 522
Publisher DOI: 10.3390/math8040522
EISSN: 2227-7390
Appears in Collections:FG Geodäsie und Ausgleichungsrechnung » Publications

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