Global Navigation Satellite System Partial Ambiguity Resolution Method Integrating Ionospheric Delay Correction and Multi-frequency Signal Optimization

ZHANG Xu; YANG Jie

doi:10.11999/JEIT240682

Volume 47 Issue 5

May 2025

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Article Navigation > Journal of Electronics & Information Technology > 2025 > 47(5): 1543-1553

ZHANG Xu, YANG Jie. Global Navigation Satellite System Partial Ambiguity Resolution Method Integrating Ionospheric Delay Correction and Multi-frequency Signal Optimization[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1543-1553. doi: 10.11999/JEIT240682

Citation:

ZHANG Xu, YANG Jie. Global Navigation Satellite System Partial Ambiguity Resolution Method Integrating Ionospheric Delay Correction and Multi-frequency Signal Optimization[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1543-1553. doi: 10.11999/JEIT240682

Citation:

ZHANG Xu, YANG Jie. Global Navigation Satellite System Partial Ambiguity Resolution Method Integrating Ionospheric Delay Correction and Multi-frequency Signal Optimization[J]. Journal of Electronics & Information Technology, 2025, 47(5): 1543-1553. doi: 10.11999/JEIT240682

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Global Navigation Satellite System Partial Ambiguity Resolution Method Integrating Ionospheric Delay Correction and Multi-frequency Signal Optimization

doi: 10.11999/JEIT240682 cstr: 32379.14.JEIT240682

ZHANG Xu^{1, 2},
YANG Jie^{2
,
,}

1.
School of Electrical and Information Engineering, Anhui University of Science and Technology, Huainan 232001, China
2.
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China

Funds: The Scientific Research Foundation for High-level Talents of Anhui University of Science and Technology (2024yjrc177), The National Key Research and Development Program of China (2020YFB1710800), The National Natural Science Foundation of China (51879211)

Received Date: 2024-07-31
Rev Recd Date: 2025-04-08

Available Online: 2025-04-29

Publish Date: 2025-05-01

Abstract

Abstract

Objective Global Navigation Satellite System (GNSS) high-precision positioning is widely applied due to its accuracy. However, the integrity of the Ambiguity Resolution (AR) process remains limited, particularly in occluded environments and over long baselines. Traditional AR methods are often affected by ionospheric delay errors, which become substantial when the ionospheric conditions differ between reference and rover stations. This paper proposes a Modified Partial Ambiguity Resolution (MPAR) method that integrates ionospheric delay correction models with multi-frequency signal optimization. The combined approach improves GNSS positioning accuracy and reliability under varied environmental and baseline conditions. Methods To reduce the effect of ionospheric delay on AR, this study incorporates an Ionospheric delay correction model into the geometry-free Cascade Integer Resolution (ICIR) method. ICIR resolves the integer ambiguities of Extra-Wide Lane (EWL), Wide Lane (WL), and Narrow Lane (NL) combinations using carrier phase measurements with different wavelengths. The ionospheric delay correction model enables compensation for differential delays between stations, improving AR accuracy, particularly over long baselines. To further enhance data usage—especially in cases of low-quality observations—a two-stage partial AR strategy is employed. In the first stage, the ICIR method is applied to an optimal subset of satellites selected based on tri-frequency availability and high elevation angles. For the non-optimal subset, which may include satellites with limited frequencies or weaker signal quality, the Least-Squares AMBiguity Decorrelation Adjustment (LAMBDA) method is used in geometric mode, with assistance from the ambiguity-fixed results of the optimal subset. This integrated approach reduces computational complexity and improves the AR success rate and reliability. The MPAR method proceeds as follows: (1) select the optimal satellite subset based on frequency availability and elevation angle; (2) apply the ICIR method to resolve ambiguities in this subset; (3) use the fixed ambiguities from the optimal subset to assist in resolving ambiguities for the non-optimal subset via the LAMBDA method; (4) obtain the final integer ambiguity solution for the full epoch. Results and Discussions The proposed MPAR method is validated using two datasets collected under different environments: one from Tokyo, characterized by complex urban occlusion and long baselines (approximately 1 700 meters), and another from Wuhan, featuring an open campus environment and short baselines (approximately 600 meters). The results show that the MPAR method outperforms traditional PAR methods in positioning accuracy, AR success rate, and computational efficiency. As shown in (Fig. 3) and (Fig. 5), satellite visibility in the Tokyo dataset is significantly affected by occlusion, leading to fewer available satellites compared to the Wuhan dataset. Despite these challenges, the MPAR method achieves the highest success rate and the lowest Average Standard Deviation (ASD) in all tested scenarios, including GPS, BDS, and dual-system modes (Table 2 and Table 4). In the Tokyo dataset, the MPAR method reduces the ASD by up to 40% compared to traditional methods, reflecting its robustness in complex environments. The AR success rate also significantly improves with the MPAR method. As presented in (Table 3) and (Table 5), the MPAR method achieves AR success rates exceeding 86% in all tested scenarios, with a peak rate of 99.4% in the GPS/BDS dual-system mode of the Wuhan dataset. These results demonstrate the effectiveness of the proposed method in enhancing AR reliability under challenging conditions. In terms of computational efficiency, the MPAR method exhibits balanced performance. Although the use of the ionospheric delay correction model slightly increases computational complexity, the overall efficiency remains competitive, with an average solution time of approximately 0.13 seconds per epoch (Table 3 and Table 5). This performance supports the suitability of the MPAR method for real-time applications. Furthermore, Table 3 (Tokyo dataset) and Table 5 (Wuhan dataset) summarize the performance metrics of the five AR methods evaluated. The MPARICIR method achieves the highest AR success rates across all systems and environments, reaching 93.1% and 99.4% for the Tokyo and Wuhan datasets, respectively. Notably, the MPARICIR method maintains a high success rate while reducing computation time compared to other methods, indicating its efficiency. These results support the effectiveness and robustness of the proposed MPARICIR method in improving GNSS positioning performance. Conclusions This study proposes an MPAR method for high-precision GNSS positioning. By integrating ionospheric delay correction models with multi-frequency signal optimization, MPAR combines the strengths of geometry-free and geometry-based AR strategies. The method effectively reduces the effect of ionospheric delay, particularly over long baselines and in occluded environments. Experimental results confirm that MPAR improves positioning accuracy, AR success rate, and computational efficiency relative to conventional methods. Its consistent performance across varied environments and baseline lengths highlights its suitability for broad application in high-precision GNSS positioning.

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References(20)

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