Next Article in Journal
Estimating Ionospheric Phase Scintillation Indices in the Polar Region from 1 Hz GNSS Observations Using Machine Learning
Next Article in Special Issue
Cascaded Ambiguity Resolution for Pseudolite System-Augmented GNSS PPP
Previous Article in Journal
Geomorphological Evidence of Ice Activity on Mars Surface at Mid-Latitudes
Previous Article in Special Issue
Robust and Adaptive Ambiguity Resolution Strategy in Continuous Time and Frequency Transfer
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Remote Sensing
Volume 17
Issue 17
10.3390/rs17173071
remotesensing-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Variance Component Estimation (VCE)-Based Adaptive Stochastic Modeling for Enhanced Convergence and Robustness in GNSS Precise Point Positioning (PPP)

by
Yanning Zheng
1,2,
Yongfu Sun
1,
Yubin Zhou
1,*,
Shengli Wang
3 and
Yixu Liu
1,2
1
National Deep Sea Center (NDSC), Qingdao 266237, China
2
College of Environmental Science and Engineering, Ocean University of China, Qingdao 266100, China
3
College of Ocean Science and Engineering, Shandong University of Science and Technology, Qingdao 266590, China
*
Author to whom correspondence should be addressed.
Remote Sens. 2025, 17(17), 3071; https://doi.org/10.3390/rs17173071
Submission received: 22 July 2025 / Revised: 25 August 2025 / Accepted: 26 August 2025 / Published: 3 September 2025
(This article belongs to the Special Issue Advancing Positioning, Navigation, and Timing (PNT) Service Using Satellite Navigation Technology)
Browse Figures
Review Reports Versions Notes

Abstract

The stochastic model in Precise Point Positioning (PPP) defines the statistical properties of observations and the dynamic behavior of parameters. An inaccurate stochastic model can degrade positioning accuracy, ambiguity resolution, and other aspects of performance. However, due to the influence of multiple factors, the stochastic model in PPP cannot be precisely predetermined, necessitating the development of an Adaptive Stochastic Model (ASM) based on Variance Component Estimation (VCE). While the benefits of ASMs for PPP float solutions are well documented, their contributions to other performance aspects remain insufficiently explored. This paper presents a comprehensive assessment of an ASM’s impact on PPP. First, the implementation of an ASM using VCE is described in detail. Then, experimental results demonstrate that the ASM effectively captures observational conditions through the estimated variance component factors. It enhances both PPP float and fixed solutions when the predefined stochastic model is inadequate, improves cycle-slip detection by tightening the stochastic model (reducing the missed detection rate from 19% to 8%), and accelerates both direct reconvergence and re-initialization after data gaps, with reconvergence times improved by 18% and 55%, respectively.
Keywords: stochastic model; Precise Point Positioning (PPP); Variance Component Estimation (VCE); Ambiguity Resolution (AR); Detection, Identification, Adaptation (DIA); interruption fixing stochastic model; Precise Point Positioning (PPP); Variance Component Estimation (VCE); Ambiguity Resolution (AR); Detection, Identification, Adaptation (DIA); interruption fixing

Share and Cite

MDPI and ACS Style

Zheng, Y.; Sun, Y.; Zhou, Y.; Wang, S.; Liu, Y. Variance Component Estimation (VCE)-Based Adaptive Stochastic Modeling for Enhanced Convergence and Robustness in GNSS Precise Point Positioning (PPP). Remote Sens. 2025, 17, 3071. https://doi.org/10.3390/rs17173071

AMA Style

Zheng Y, Sun Y, Zhou Y, Wang S, Liu Y. Variance Component Estimation (VCE)-Based Adaptive Stochastic Modeling for Enhanced Convergence and Robustness in GNSS Precise Point Positioning (PPP). Remote Sensing. 2025; 17(17):3071. https://doi.org/10.3390/rs17173071

Chicago/Turabian Style

Zheng, Yanning, Yongfu Sun, Yubin Zhou, Shengli Wang, and Yixu Liu. 2025. "Variance Component Estimation (VCE)-Based Adaptive Stochastic Modeling for Enhanced Convergence and Robustness in GNSS Precise Point Positioning (PPP)" Remote Sensing 17, no. 17: 3071. https://doi.org/10.3390/rs17173071

APA Style

Zheng, Y., Sun, Y., Zhou, Y., Wang, S., & Liu, Y. (2025). Variance Component Estimation (VCE)-Based Adaptive Stochastic Modeling for Enhanced Convergence and Robustness in GNSS Precise Point Positioning (PPP). Remote Sensing, 17(17), 3071. https://doi.org/10.3390/rs17173071

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop