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GNSS Advanced Positioning Algorithms and Innovative Applications

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A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Environmental Remote Sensing".

Deadline for manuscript submissions: closed (26 May 2024) | Viewed by 13140

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Special Issue Editors

Prof. Dr. Anja Heßelbarth

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Guest Editor

Faculty of Spatial Information, Dresden University of Applied Science, Dresden, Germany
Interests: global navigation satellite systems; precise positioning and navigation algorithms; geodesy; low-cost-GNSS; GNSS-data analysis

Dr. Daniel Medina

E-Mail Website
Guest Editor

German Aerospace Center (DLR), Neustrelitz, Germany
Interests: GNSS; precise positioning; robust filtering; attitude estimation; sensor fusion

Special Issue Information

Dear Colleagues,

Global navigation satellite systems (GNSSs) play a fundamental role in our everyday lives, having become the main source of information for timing and outdoor positioning. The nominal open sky performance for GNSS algorithms allow for meter- and centimeter-level accuracies, based on the use of code and carrier phase observations, respectively. However, autonomous systems and other safety-critical applications present more stringent requirements for the reliability, continuity and precision of the navigation solution.

This Special Issue focuses on the study of advanced processing schemes for robust and/or high-precision GNSS solutions. This includes innovations related to precise point positioning (PPP) and real-time kinematic (RTK) schemes, the use of low-cost receivers and smartphones as alternatives for geodetic equipment, the use of new correction services, and the detection and exclusion of outliers in harsh signal propagation environments. Furthermore, contributions related to innovative applications for GNSS and experimental and testbed demonstrations will be highly appreciated.

Prof. Dr. Anja Heßelbarth
Dr. Daniel Medina
Guest Editors

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Keywords

high-precision positioning and navigation
precise point positioning (PPP) and real-time kinematic (RTK)
smartphone GNSS or low-cost GNSS with high accuracies
new correction services (Galileo HAS, PPP-SSR)
robust estimation and multipath mitigation
integrity monitoring
collaborative localization
multi-antenna GNSS and attitude estimation
innovative applications for GNSS

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GNSS Advanced Positioning Algorithms and Innovative Applications (2nd Edition) in Remote Sensing

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21 pages, 11486 KiB

Open AccessArticle

Performance of Smartphone BDS-3/GPS/Galileo Multi-Frequency Ionosphere-Free Precise Code Positioning

by Ruiguang Wang, Chao Hu, Zhongyuan Wang, Fang Yuan and Yangyang Wang

Remote Sens. 2023, 15(22), 5371; https://doi.org/10.3390/rs15225371 - 15 Nov 2023

Cited by 1 | Viewed by 1616

Abstract

The continuously improving performance of mass-market global navigation satellite system (GNSS) chipsets is enabling the prospect of high-precision GNSS positioning for smartphones. Nevertheless, a substantial portion of Android smartphones lack the capability to access raw carrier phase observations. Therefore, this paper introduces a precise code positioning (PCP) method, which utilizes Doppler-smoothed pseudo-range and inter-satellite single-difference methods. For the first time, the results of a quality investigation involving BDS-3 B1C/B2a/B1I, GPS L1/L5, and Galileo E1/E5a observed using smartphones are presented. The results indicated that Xiaomi 11 Lite (Mi11) exhibited a superior satellite data decoding performance compared to Huawei P40 (HP40), but it lagged behind HP40 in terms of satellite tracking. In the static open-sky scenario, the carrier-to-noise ratio (CNR) values were mostly above 25 dB-Hz. Additionally, for B1C/B1I/L1/E1, they were approximately 8 dB-Hz higher than those for B2a/L5/E5a. Second, various PCP models were developed to address ionospheric delay. These models include the IF-P models, which combine traditional dual-frequency IF pseudo-ranges with single-frequency ionosphere-corrected pseudo-ranges using precise ionospheric products, and IFUC models, which rely solely on single-frequency ionosphere-corrected pseudo-ranges. Finally, static and dynamic tests were conducted using datasets collected from various real-world scenarios. The static tests demonstrated that the PCP models could achieve sub-meter-level accuracy in the east (E) and north (N) directions, while achieving meter-level accuracy in the upward (U) direction. Numerically, the root mean square error (RMSE) improvement percentages were approximately 93.8%, 75%, and 82.8% for HP40 in the E, N, and U directions, respectively, in both open-sky and complex scenarios compared to single-point positioning (SPP). In the open-sky scenario, Mi11 showed an average increase of about 85.6%, 87%, and 16% in the E, N, and U directions, respectively, compared to SPP. In complex scenarios, Mi11 exhibited an average increase of roughly 68%, 75.9%, and 90% in the E, N, and U directions, respectively, compared to SPP. Dynamic tests showed that the PCP models only provided an improvement of approximately 10% in the horizontal plane or U direction compared to SPP. The triple-frequency IFUC (IFUC123) model outperforms others due to its lower noise and utilization of multi-frequency pseudo-ranges. The PCP models can enhance smartphone positioning accuracy. Full article

(This article belongs to the Special Issue GNSS Advanced Positioning Algorithms and Innovative Applications)

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20 pages, 575 KiB

Open AccessArticle

Signal Occlusion-Resistant Satellite Selection for Global Navigation Applications Using Large-Scale LEO Constellations

by Junqi Guo, Yang Wang and Chenyang Sun

Remote Sens. 2023, 15(20), 4978; https://doi.org/10.3390/rs15204978 - 16 Oct 2023

Cited by 2 | Viewed by 1266

Abstract

With the continuous construction of large-scale Low Earth Orbit (LEO) constellations, their potential for Global Navigation Satellite System (GNSS) applications has been emphasized. This study aims to derive an optimal positioning configuration formula based on the ratio of high-elevation and low-elevation satellites, which can improve the positioning accuracy and overcome the accuracy loss due to signal occlusion. A genetic algorithm is used to solve the optimal positioning configuration problem for large-scale satellite selection. Through a simulation using Starlink satellites currently in orbit, it is verified that the traditional recursive algorithm cannot be applied to satellite selection for large-scale constellations. The proposed formula has a similar accuracy to the Quasi-Optimal algorithm when there is no signal occlusion and the satellites are uniformly selected. However, the accuracy of the latter deteriorates significantly under signal occlusion. Our algorithm can effectively overcome this problem. Moreover, we discuss the effect of different types of obstructions on the accuracy loss. We find that the Quasi-Optimal algorithm is more sensitive to a single large-angle obstruction than multiple small-angle obstructions. Our proposed formula can reduce the localization accuracy degradation caused by signal occlusions in both scenarios. Full article

(This article belongs to the Special Issue GNSS Advanced Positioning Algorithms and Innovative Applications)

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20 pages, 28641 KiB

Open AccessArticle

PPP-RTK with Rapid Convergence Based on SSR Corrections and Its Application in Transportation

by Xiangdong An, Ralf Ziebold and Christoph Lass

Remote Sens. 2023, 15(19), 4770; https://doi.org/10.3390/rs15194770 - 29 Sep 2023

Cited by 3 | Viewed by 2579

Abstract

Real-time Kinematic (RTK) positioning provides centimeter-level positioning accuracy within several seconds, but it has to rely on a nearby base station. Although Precise Point Positioning (PPP) supplies precise positions with one receiver, its convergence time takes several tens of minutes, which makes PPP not well suited for real-time kinematic applications where a rapid convergence is required. PPP-RTK integrates the benefits of PPP and RTK, which actually is PPP augmented by a ground GNSS network. The satellite orbit, clock offsets, signal biases, ionospheric and tropospheric corrections are determined based on this GNSS network, modeled as state space information and transmitted to PPP users. By applying these State Space Representation (SSR) corrections, a real-time kinematic PPP-RTK approach is developed and implemented, which can instantly resolve the ambiguities to integers and realize rapid convergence. In a static scenario, it realized an instant ambiguity resolution and a rapid convergence within 2 s in more than 90% of 120 hourly sessions. The PPP-RTK has been applied and evaluated in a kinematic scenario on the highway. The horizontal positioning errors are almost lower than 0.1 m except for the time of passing through bridges. After passing bridges, the PPP-RTK successfully resolved ambiguities within 2 s in 90.6% of the cases and achieved convergence in horizontal within 5 s in more than 90% of the cases. The PPP-RTK with a precision of 0.1 m and rapid convergence of several seconds benefits the precise navigation of automobile on the highway, which will support the development of autonomous driving in future. Full article

(This article belongs to the Special Issue GNSS Advanced Positioning Algorithms and Innovative Applications)

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29 pages, 9686 KiB

Open AccessArticle

Joint Retrieval of Sea Surface Rainfall Intensity, Wind Speed, and Wave Height Based on Spaceborne GNSS-R: A Case Study of the Oceans near China

by Jinwei Bu, Kegen Yu, Feiyang Zhu, Xiaoqing Zuo and Weimin Huang

Remote Sens. 2023, 15(11), 2757; https://doi.org/10.3390/rs15112757 - 25 May 2023

Cited by 6 | Viewed by 2256

Abstract

In this paper, a method for joint sea surface rainfall intensity (RI), wind speed, and wave height retrieval based on spaceborne global navigation satellite system reflectometry (GNSS-R) data is proposed, which especially considers the effects between these two parameters. A method of rainfall detection (RD) according to different wind speed ranges is also proposed by mitigating the impact of swell and wind speed. The results, with data collected over the oceans near Southeast Asia, show that the RD method has a detection accuracy of up to 81.74%. The RI retrieval accuracy can reach about 2 mm/h by simultaneously correcting the effects of wind speed and swell. The accuracy of wind speed retrieval is improved by about 5% after removing rainfall interference through RD in advance. After considering the influence of wind speed and eliminating rainfall interference, the retrieval accuracy of significant wave height (SWH) is improved by about 18%. Finally, the deep convolutional neural network (DCNN) model is built to estimate the SWH of the swell. The results show that the retrieval accuracy of the swell height is better than 0.20 m after excluding rainfall interference. The proposed joint retrieval method provides an important reference for the future acquisition of multiple high-precision marine geophysical parameters by spaceborne GNSS-R technology. Full article

(This article belongs to the Special Issue GNSS Advanced Positioning Algorithms and Innovative Applications)

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22 pages, 6749 KiB

Open AccessArticle

Improving Smartphone GNSS Positioning Accuracy Using Inequality Constraints

by Zihan Peng, Yang Gao, Chengfa Gao, Rui Shang and Lu Gan

Remote Sens. 2023, 15(8), 2062; https://doi.org/10.3390/rs15082062 - 13 Apr 2023

Cited by 5 | Viewed by 2842

Abstract

To improve smartphone GNSS positioning performance using extra inequality information, an inequality constraint method was introduced and verified in this study. Firstly, the positioning model was reviewed and three constraint applications were derived from it, namely, vertical velocity, direction, and distance constraints. Secondly, we introduced an estimator based on the density function truncation method to solve the inequality constraint problem. Finally, the performance of the method was investigated using datasets from three smartphones, including a Huawei P30, a Huawei P40, and a Xiaomi MI8. The results indicate that the position and velocity accuracy can be improved in the up component using a vertical velocity constraint. The horizontal positioning accuracy was increased using a heading direction constraint with dynamic datasets. Numerically, the root mean square error (RMSE) improvement percentages were 16.77%, 14.57%, and 31.09% for HP40, HP30, and XMI8, respectively. Using an inter-smartphone distance constraint could enhance the horizontal positioning of all participating smartphones, with improvement percentages of 34.27%, 75.58%, and 23.66% for HP40, HP30, and XMI8, respectively, in the static dataset. Additionally, the improvement percentages were 15.90%, 5.55%, and 0.17% in dynamic datasets. In summary, this study demonstrates that utilizing inequality constraints can significantly improve smartphone GNSS positioning. Full article

(This article belongs to the Special Issue GNSS Advanced Positioning Algorithms and Innovative Applications)

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18 pages, 5339 KiB

Open AccessTechnical Note

Robust GNSS Positioning Using Unbiased Finite Impulse Response Filter

by Jie Dou, Bing Xu and Lei Dou

Remote Sens. 2023, 15(18), 4528; https://doi.org/10.3390/rs15184528 - 14 Sep 2023

Viewed by 1319

Abstract

In a typical GNSS receiver, pseudorange and pseudorange rate measurements are generated through the code and carrier tracking loops, respectively. These measurements are then employed to calculate the user’s position and velocity (PV) solutions, which is typically achieved using a Kalman filter (KF) or the least squares (LS) algorithm. However, the LS method only uses the current observation without error analysis. The positioning result is greatly affected by the errors in the observed data. In KF, by using an iterative approach that combines predictions and measurements of PV information, more accurate estimates can be obtained because the PV information is time-correlated. Meanwhile, its optimal estimate requires that both the model and noise statistics are exactly known. Otherwise, achieving optimality cannot be guaranteed. To address this issue, this paper proposes and implements a novel GNSS solution method based on an unbiased finite impulse response (UFIR) filter. Two different field tests were conducted. The position results of UFIR are compared with those from the LS and KF methods, and the horizon positioning mean error is improved by 44% and 29%, respectively, which highlights its efficacy. The method offers two primary benefits: it is robust to noise uncertainty, and it leverages historical data within the UFIR framework to provide a more accurate estimate of the current state. Full article

(This article belongs to the Special Issue GNSS Advanced Positioning Algorithms and Innovative Applications)

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