Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation

Li, Qin; Yao, Wanqiang; Tu, Rui; Du, Yanjun; Liu, Mingyue

doi:10.3390/rs15122958

Cited by 4 publications

(4 citation statements)

References 26 publications

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“…For instance, the precision clock products for GPS, Galileo, and BDS3 are derived using the frequencies L1/L2, E1/E5a, and B1I/B3I [32]. Referring to equations (1) and (2), the IF combination observations can be expressed as [33]:…”

Section: Observation Modelmentioning

confidence: 99%

Validation and assessment of multi-GNSS phase bias products from IGS analysis centers

Lyu,

Wang,

et al. 2024

Meas. Sci. Technol.

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Phase bias plays a crucial role in Precision Point Positioning (PPP) with ambiguity resolution. Currently, an increasing number of analysis centers are being of releasing multi-GNSS phase bias products. However, it remains uncertain how different phase bias products, receiver types, and GNSS systems combinations might impact user-side positioning performance and ambiguity resolution capabilities. In view of this, this contribution systematically investigates the effects on PPP with ambiguity resolution. Four types of receivers, including SEPT POLARX5, LEICA GR50, JAVAD TRE_3 DELTA, and TRIMBLE ALLOY, are selected, and six types of GNSS system combinations, namely, GPS, Galileo, BDS3, GPS/Galileo, GPS/BDS3, GPS/Galileo/BDS3, are used, and the phase bias products from OSB-CODE, OSB-CNES/CLS, OSB-GFZ, OSB-WHU, OSB-CNES/NAV, IRC-CNES/CLS, IRC-GFZ are adopted for the numerical experiment. Compared with the float-solution, the results show that the accuracy of PPP fixed-solution with single-system can be improved 1cm~3cm, and the convergence time is reduced 2min~ 15min. The positioning performance using the seven phase bias products for any single-system satisfies the following order from the best to the worst: OSB_WHU > OSB_CODE > OSB_CNES/CLS ≈ OSB_CNES/NAV > OSB_GFZ > IRC_CNES/CLS ≈ IRC_GFZ, but the performance of ambiguity resolution do not present certain characteristics. Meanwhile, the ambiguity-fix success-rate and TTFF can keep at 80%~96% and 20min~40min with single-system, respectively. In the case of multi-system, although the difference in the positioning performance among the phase bias products is not significant, the multi-system outperforms the single-systemin terms of both positioning performance and ambiguity resolution. However, the difference between the dual-system and the triple-system is smaller. In addition, the positioning performance corresponding to different receiver types also shows distinction, while there is no significant difference in the ambiguity resolution performance between different receiver types.

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Section: Observation Modelmentioning

confidence: 99%

Validation and assessment of multi-GNSS phase bias products from IGS analysis centers

Lyu,

Wang,

et al. 2024

Meas. Sci. Technol.

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“…The received optical power by the PD is shown in Equation ( 1). (1) where is the distance between the LED and the receiver, is the effective area of the PD at the receiver, is the optical power of the LED source, is the angle of irradiance, is the angle of incidence at the receiver, is the gain of an optical filter, and is the gain of an optical concentrator placed in front of the sensor [19]. Generally speaking, when there is no lens or filter in front of the PD, .…”

Section: Principle Of the Proposed Vlp Systemmentioning

confidence: 99%

“…The Global Positioning System (GPS) is a widely used positioning technology [1], but it suffers from significant attenuation in challenging environments due to various interference factors such as trees, walls, and ceilings. According to Kerem Özsoy [2], GPS indoor localization can reach an accuracy of only approximately 7 m with the help of power amplifiers under various types of interference.…”

Section: Introductionmentioning

confidence: 99%

Diffuse Reflection Effects in Visible Light Positioning: Analysis, Modeling, and Evaluation

Zhang,

Yang,

Sun

et al. 2023

Electronics

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Currently, the Global Positioning System (GPS) is widely used, but its signal is attenuated by factors such as trees, walls, and ceilings, which severely degrade its positioning accuracy. To fill the gap, various indoor positioning techniques have attracted increasing attention in recent years. Visible light positioning (VLP) is a promising scheme for indoor positioning due to its high precision, high security, and low energy consumption; however, ubiquitous diffuse reflection affects the accuracy and robustness of VLP. During our testing, we found that diffuse reflection could increase the error in RSS values by 20~30%, severely affecting VLP accuracy; however, diffuse reflection is inevitable in real positioning environments. To solve this problem, this paper first establishes a wall diffuse reflection model and then implements a visible light positioning system based on an Internet of Things platform. Finally, this paper uses the system to verify the effectiveness of the diffuse reflection model. The experiments show that the proposed model effectively improves positioning accuracy by 36.7~61.3%.

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“…Recent advancements in Low-Earth-Orbit (LEO) constellations have significantly impacted satellite navigation systems due to their lower orbital altitude, which minimizes signal path attenuation and maximizes signal reception power [1]. Utilizing LEO constellations for navigation enhancement could substantially improve the performance of existing satellite navigation systems [2].…”

Section: Introductionmentioning

confidence: 99%

Navigation Resource Allocation Algorithm for LEO Constellations Based on Dynamic Programming

Wang,

Tang,

et al. 2024

Remote Sensing

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Navigation resource allocation for low-earth-orbit (LEO) constellations refers to the optimal allocation of navigational assets when the number and allocation of satellites in the LEO constellation have been determined. LEO constellations can not only transmit navigation enhancement signals but also enable space-based monitoring (SBM) for real-time assessment of GNSS signal quality. However, proximity in the frequencies of LEO navigation signals and SBM can lead to significant interference, necessitating isolated transmission and reception. This separation requires that SBM and navigation signal transmission be carried out by different satellites within the constellation, thus demanding a strategic allocation of satellite resources. Given the vast number of satellites and their rapid movement, the visibility among LEO, medium-earth-orbit (MEO), and geostationary orbit (GEO) satellites is highly dynamic, presenting substantial challenges in resource allocation due to the computational intensity involved. Therefore, this paper proposes an optimal allocation algorithm for LEO constellation navigation resources based on dynamic programming. In this algorithm, a network model for the allocation of navigation resources in LEO constellations is initially established. Under the constraints of visibility time windows and onboard transmission and reception isolation, the objective is set to minimize the number of LEO satellites used while achieving effective navigation signal transmission and SBM. The constraints of resource allocation and the mathematical expression of the optimization objective are derived. A dynamic programming approach is then employed to determine the optimal resource allocation scheme. Analytical results demonstrate that compared to Greedy and Divide-and-Conquer algorithms, this algorithm achieves the highest resource utilization rate and the lowest computational complexity, making it highly valuable for future resource allocation in LEO constellations.

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Performance Assessment of Multi-GNSS PPP Ambiguity Resolution with LEO-Augmentation

Cited by 4 publications

References 26 publications

Validation and assessment of multi-GNSS phase bias products from IGS analysis centers

Validation and assessment of multi-GNSS phase bias products from IGS analysis centers

Diffuse Reflection Effects in Visible Light Positioning: Analysis, Modeling, and Evaluation

Navigation Resource Allocation Algorithm for LEO Constellations Based on Dynamic Programming

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