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An Entropy-Weighted Multi-Factor Ambiguity Subset Selection Algorithm for Partial Ambiguity Resolution in Multi-GNSS
Mingduan Zhou1, Lu Qin1, Likun Cui2
1School of Geomatics and Urban Spatial Informatics, Beijing University of Civil Engineering and Architecture, Beijing 102616, China.
Sensors (Basel, Switzerland)
|July 28, 2026
Summary
This study introduces a novel multi-factor partial ambiguity resolution (MPAR) algorithm for precise point positioning (PPP). MPAR enhances ambiguity subset selection, improving reliability and convergence in multi-GNSS and multi-frequency scenarios.
Area of Science:
- Geodesy and Geomatics Engineering
- Satellite Navigation Systems
Background:
- Precise Point Positioning (PPP) requires reliable ambiguity resolution for high accuracy.
- Multi-GNSS and multi-frequency systems increase ambiguity search space, challenging traditional methods.
- Partial Ambiguity Resolution (PAR) is crucial for managing high-dimensional ambiguity scenarios.
Purpose of the Study:
- To develop an improved algorithm for selecting ambiguity subsets in multi-GNSS, multi-frequency PPP.
- To enhance the reliability and convergence speed of precise point positioning through better ambiguity resolution.
- To address the challenges posed by high-dimensional ambiguity search spaces in undifferenced and uncombined (UDUC) PPP.
Main Methods:
- Proposed a multi-factor ranking and screening partial ambiguity resolution (MPAR) algorithm.
- Utilized entropy-weighted multi-factor assessment including signal-to-noise ratio, ambiguity variance, and carrier-phase residual.
- Implemented min-max normalization and adaptive entropy weighting for indicator integration.
- Employed iterative screening for hard-to-fix ambiguities before integer fixing.
Main Results:
- MPAR achieved high ambiguity-fixing rates (98.9%, 98.7%, 99.2%) across three- to five-frequency configurations.
- Demonstrated comparable performance to elevation-angle-factor-based PAR (ELE) and superior performance over variance-factor-based PAR (VAR) and full ambiguity resolution (FAR).
- Significantly improved ambiguity residuals (wide-lane: +13.6%, narrow-lane: +46.4%) compared to VAR.
- Achieved rapid convergence times (9.1 min horizontal, 8.1 min vertical) under five-frequency conditions.
Conclusions:
- The proposed entropy-weighted multi-factor subset selection algorithm effectively improves ambiguity estimation quality.
- MPAR enhances the reliability and convergence performance of high-dimensional multi-GNSS and multi-frequency PPP.
- This method offers a robust solution for complex ambiguity resolution challenges in modern satellite navigation.
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