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A multipath error is one of the main sources of GNSS positioning errors. It cannot be eliminated by forming double-difference and other methods, and it has become an issue in GNSS positioning error processing, because it is mainly related to the surrounding environment of the station. To address multipath errors, three main mitigation strategies are employed: site selection, hardware enhancements, and data processing. Among these, data processing methods have been a focal point of research due to their cost-effectiveness, impressive performance, and widespread applicability. This paper focuses on the review of data processing mitigation methods for GNSS carrier-phase multipath errors. The paper begins by elucidating the origins and mitigation strategies of multipath errors. Subsequently, it reviews the current research status pertaining to data processing methods using stochastic and functional models to counter multipath errors. The paper also provides an overview of filtering techniques for extracting multipath error models from coordinate sequences or observations. Additionally, it introduces the evolution and algorithmic workflow of sidereal filtering (SF) and multipath hemispherical mapping (MHM), from both coordinate and observation domain perspectives. Furthermore, the paper emphasizes the practical significance and research relevance of multipath error processing. It concludes by delineating future research directions in the realm of multipath error mitigation. [ABSTRACT FROM AUTHOR]

Cableways have been widely used in industrial areas, cities, and scenic spots due to their advantages, such as being a convenient mode of transportation, time-saving, labor-saving, and low cost, as well as offering environmental protection. To ensure the safe operation of a cableway, based on the characteristic that the velocity of the cableway bracket is approximately zero in a static deformation monitoring environment, a deformation monitoring method called zero velocity update (ZUPT)-based GNSS/IMU tightly coupled algorithm with the constraint of the Earth's rotation angular velocity was proposed. The proposed method can effectively solve the problem of a single GNSS being unable to output attitude, which is directly related to the status of wire ropes and cable cars. Meanwhile, ZUPT is used to restrain the Kalman filter's divergence when IMU is stationary. However, the improvements of ZUPT on attitude are not obvious, so the constraint of the Earth's rotation angular velocity was applied. The performance of the proposed method was evaluated through monitoring the cableway bracket of the Yimeng Mountain Tourism area in Shandong. Compared with the ZUPT-based GNSS/IMU tightly coupled algorithm (ZUPT-TC), the proposed method can further constrain the error accumulation of IMU while stationary and, therefore, it can provide reliable position and attitude information on cableway brackets. [ABSTRACT FROM AUTHOR]

The BDS multipath delay error is highly related to the surrounding monitoring environment, which cannot be eliminated or mitigated by applying the double difference observation model. In the actual monitoring environment, due to the complexity of the BDS constellation, it is difficult for existing algorithms to consider GEO, IGSO, MEO and other different orbital types of satellites for real-time and efficient multipath error reduction. Therefore, we propose a novel BDS dual-frequency multipath error reduction method for real deformation monitoring for BDS considering various satellite orbit types. This method extracts the single error residual of each satellite based on the assumption of "zero mean" and divides the appropriate grid density of GEO and IGSO/MEO, respectively, to construct a dual-frequency multipath hemispherical map model suitable for BDS satellites with different orbital types. This method can realize the multipath error elimination of the observed values of different orbits and different frequencies. The results of simulation experiments and real deformation monitoring data demonstrate that this method can effectively eliminate low-frequency multipath delay errors in the observation domain and coordinate domain. After multipath correction, the precision of the horizontal coordinates and height coordinates are 1.7 mm and 4.6 mm. The precision of the horizontal coordinate and height coordinate is increased by 50% and 60%, respectively. The fixed rate of ambiguity increased by 5–7%. [ABSTRACT FROM AUTHOR]

The accuracy and integrity of structural deformation monitoring can be improved by the GNSS/accelerometer integrated system, and gross error detection is the key to further improving the reliability of GNSS/accelerometer monitoring. Traditional gross error detection methods assume that real-state information is known, and they need to establish state iterators, which leads to low computational efficiency. Meanwhile, in multi-sensor fusion, if the sampling rates are different, the change in the dimension of the observation matrix must be considered, and the calculation is complex. Based on state-domain consistency theory, this paper proposes the State-domain Robust Autonomous Integrity Monitoring by Extrapolation (SRAIME) method for identifying slow-growing gross errors for GNSS/accelerometer integrated deformation monitoring. Compared with the traditional gross error detection method, the proposed method constructs state test statistics based on the state estimated value and the state predicted value, and it directly performs gross error identification in the state domain. This paper deduces the feasibility of the proposed method theoretically and verifies that the proposed method does not need to consider the dimension change of the observation matrix in gross error detection. Meanwhile, in the excitation deformation experiments of the Suntuan River Bridge in Anhui and the Wilford Bridge in the United Kingdom, the slow gradient of the slope was added to the measurement domain, and the traditional AIME method and the method proposed in this paper were adopted for the gross error identification of the GNSS/accelerometer fusion process. The results demonstrate that both methods can effectively detect gross errors, but the proposed method does not need to consider the dimensional change in the observation matrix during the fusion process, which has better applicability to GNSS/accelerometer integrated deformation monitoring. [ABSTRACT FROM AUTHOR]

Low-cost GPS/INS integration system is the ideal combination of navigation and positioning. However, the sensitivity of low-cost INS is not good enough for the initial alignment in statics base before navigation. Considering this problem, this paper presents an arbitrary misalignment angle error propagation model which does not rely on small misalignment angles assumption and two simplified versions. These models are presented in the ECEF frame approach and are suitable to implement the in-motion alignment with GPS aided. Another three error models based on quaternion, Rodrigues parameters and modified Rodrigues parameters were also proposed. Three experiments were designed to verify the accuracy and computational efficiency of these error models. The experiments' results showed that the small misalignment angle model was applicable to the small misalignment angle for higher computational efficiency but without improvement of accuracy. And the large misalignment angle is the best error model for the initial alignment of the arbitrary misalignment angle. [ABSTRACT FROM AUTHOR]

Global Navigation Satellite System (GNSS) carrier phase measurement for short baseline meets the requirements of deformation monitoring of large structures. However, the carrier phase multipath effect is the main error source with double difference (DD) processing. There are lots of methods to deal with the multipath errors of Global Position System (GPS) carrier phase data. The BeiDou navigation satellite System (BDS) multipath mitigation is still a research hotspot because the unique constellation design of BDS makes it different to mitigate multipath effects compared to GPS. Multipath error periodically repeats for its strong correlation to geometry of satellites, reflective surface and antenna which is also repetitive. We analyzed the characteristics of orbital periods of BDS satellites which are consistent with multipath repeat periods of corresponding satellites. The results show that the orbital periods and multipath periods for BDS geostationary earth orbit (GEO) and inclined geosynchronous orbit (IGSO) satellites are about one day but the periods of MEO satellites are about seven days. The Kalman filter (KF) and Rauch-Tung-Striebel Smoother (RTSS) was introduced to extract the multipath models from single difference (SD) residuals with traditional sidereal filter (SF). Wavelet filter and Empirical mode decomposition (EMD) were also used to mitigate multipath effects. The experimental results show that the three filters methods all have obvious effect on improvement of baseline accuracy and the performance of KT-RTSS method is slightly better than that of wavelet filter and EMD filter. The baseline vector accuracy on east, north and up (E, N, U) components with KF-RTSS method were improved by 62.8%, 63.6%, 62.5% on day of year 280 and 57.3%, 53.4%, 55.9% on day of year 281, respectively. [ABSTRACT FROM AUTHOR]

As a typical application of geodesy, the GNSS/INS (Global Navigation Satellite System and Inertial Navigation System) integrated navigation technique was developed and has been applied for decades. For the integrated systems with multiple sensors, data fusion is one of the key problems. As a well-known data fusion algorithm, the Kalman filter can provide optimal estimates with known parameters of the models and noises. In the literature, however, the data fusion algorithm of the GNSS/INS integrated navigation and positioning systems is performed under a certain norm, and performance of the conventional filtering algorithms are improved only under this fixed and limited frame. The mixed norm-based data fusion algorithm is rarely discussed. In this paper, a mixed norm-based data fusion algorithm is proposed, and the hypothesis test statistics are constructed and adopted based on the chi-square distribution. Using the land vehicle data collected through the multi-GNSS and the IMU (Inertial Measurement Unit), the proposed algorithm is tested and compared with the conventional filtering algorithms. Results show that the influences of the outlying measurements and the uncertain noises are weakened with the proposed data fusion algorithm, and the precision of the estimates is further improved. Meanwhile, the proposed algorithm provides an open issue for geodetic applications with mixed norms. [ABSTRACT FROM AUTHOR]

A new nonlinear robust filter is proposed in this paper to deal with the outliers of an integrated Global Positioning System/Strapdown Inertial Navigation System (GPS/SINS) navigation system. The influence of different design parameters for an H∞ cubature Kalman filter is analysed. It is found that when the design parameter is small, the robustness of the filter is stronger. However, the design parameter is easily out of step in the Riccati equation and the filter easily diverges. In this respect, a singular value decomposition algorithm is employed to replace the Cholesky decomposition in the robust cubature Kalman filter. With large conditions for the design parameter, the new filter is more robust. The test results demonstrate that the proposed filter algorithm is more reliable and effective in dealing with the outliers in the data sets produced by the integrated GPS/SINS system. [ABSTRACT FROM AUTHOR]

LiDAR-based place recognition is crucial for Simultaneous Localization and Mapping (SLAM) and autonomous driving. However, existing methods face challenges in achieving rotation invariance and are hindered by a large number of parameters and complex data preprocessing steps, such as ground point removal and extensive down-sampling. These limitations hinder their practical implementation in complex real-world scenarios. To address these challenges, we propose LR-Net, a lightweight and rotation-invariant place recognition network designed to handle large-scale raw point clouds. LR-Net tackles rotation invariance by efficiently capturing sparse local neighborhood information and generating rotation-invariant features through the analysis of spatial distribution and positional information within the local region. Furthermore, we enhance the network's feature perception by incorporating residual MLP structures to encode multi-level local region features, and integrating dual-attentional weighting blocks to emphasize important regions and features. Lastly, the Generalized-Mean pooling structure is utilized to aggregate the global descriptor, enabling efficient point cloud retrieval. Meanwhile, we introduce PRmate, an independent sub-network that effectively optimizes retrieval results by analyzing the descriptor similarity and location distribution of the top 25 matching candidates from the place recognition network. We extensively evaluated the proposed methods on four large-scale datasets, including both standardized point cloud scenes (Oxford RobotCar dataset and NUS In-house dataset) and raw point cloud scenes (MulRan dataset and Kitti odometry dataset). The experimental results demonstrate that LR-Net effectively attains rotation-invariance in place recognition and achieves state-of-the-art accuracy with a lightweight model size of 0.4 M parameters. Additionally, LR-Net eliminates the need for data preprocessing, as it can directly process raw point cloud data for stable place recognition and exhibits robustness against variations in point density and noise intensity, enhancing its suitability for challenging scenarios. Moreover, PRmate is compatible with all existing point cloud-based place recognition methods and improves their performance. These advantages make LR-Net and PRmate highly suitable for robotic applications that rely on LiDAR-based place recognition. Our code is publicly available on the project website (https://github.com/zhzhang023/LR-Net). [ABSTRACT FROM AUTHOR]

Although multiple global navigation satellite systems (multi-GNSS) with more visible satellites have a high success rate, they make positioning time-consuming. Partial ambiguity resolution (PAR) can improve the efficiency of multi-GNSS; however, at present PAR cannot simultaneously achieve fast and high-precision positioning with a high success rate. Therefore, PAR based on ambiguity dilution of precision- and convex-hull-based satellite selection is proposed. The experimental results of the proposed PAR, its corresponding satellite selection algorithm, the classical PAR, and the low-cutoff-elevation-angle-based multi-GNSS show that the proposed PAR outperforms the classical PAR, i.e., it achieves fast and high-precision positioning with a success rate of 100⋅0%. Furthermore, in terms of R-ratio-test-based ambiguity validation, it improves the reliability of carrier-phase-based integrity monitoring of multi-GNSS and the corresponding satellite selection algorithms. In addition, its positioning accuracy is close to that of multi-GNSS and higher than that of the classical PAR, with maximum differences of 0⋅3 and 2⋅4 cm, respectively. The proposed single (dual) frequency-based PAR improves single/dual-frequency multi-GNSS efficiency by more than 54⋅9%/80⋅4% (42⋅0%/75⋅8%) when 14⋅4 (13⋅2) out of 24⋅4 satellites are selected. [ABSTRACT FROM AUTHOR]

The estimation of ambiguity in the global navigation satellite system/inertial navigation system (GNSS/INS) tightly coupled system is a key issue for GNSS/INS precise navigation positioning. Only when the ambiguity is solved correctly can the integrated navigation system obtain high-precision positioning results. Aiming at the problem of ambiguity parameter estimation in GNSS/INS tightly coupled system, a new strategy for ambiguity parameter estimation and elimination is proposed in this paper. Here, the ambiguity parameter is first added to the state equations of GNSS/INS in the estimation process. Then, the strategy of eliminating the parameter is used to improve efficiency. A residual test is carried out based on introducing the ambiguity parameter, thereby reducing or avoiding its influence on the filtering estimation process. Two groups of experiments were carried out and compared with GNSS positioning results. The results showed that, in the open sky observation environment, the positioning accuracy of the GNSS/INS tightly coupled method proposed in this paper was within 5 cm, and the ambiguity fixed rate was more than 97%, which is basically consistent. In a GNSS-denied environment, the positioning accuracy of the GNSS/INS tightly coupled method proposed in this paper was obviously better than that of GNSS, and the positioning accuracy in X, Y, and Z directions was improved by 82.46%, 78.87%, and 79.67%, respectively. The fixed rate of ambiguity increased from 73% to 78.57%. Therefore, in a GNSS-challenged environment, the novel strategy of the GNSS/INS tightly coupled system has higher ambiguity fixed rate and significantly improves positioning accuracy and continuity. [ABSTRACT FROM AUTHOR]

• Factor graph optimization is employed to combine observations from multi-sensors. • Proposed GNSS-to-VIO time synchronization method can adapt to the time instability of smartphones and is robust. • Our adaptive positioning mechanism enable smartphone to achieve seamless positioning. • The positioning accuracy of the proposed method is positively correlated with the precision of GNSS observations. With the diversification of functionalities and enhancement of performance in smartphones, low-cost GNSS chips, Micro-Electro-Mechanical System (MEMS) inertial measurement units (IMU), and cameras have become essential hardware components. Moreover, the computational power provided by smartphones for data processing has also become more robust. The fusion of multiple sensors in smartphones for positioning has emerged as a trend. In order to further enhance the positioning performance of smartphones in complex environments, this paper proposes a smartphone-based Vision/MEMS-IMU/GNSS tightly coupled integration for indoor-outdoor seamless positioning. Factor graph optimization is employed to combine observations from monocular vision, MEMS-IMU, and GNSS. To solve the unstable timing of current smartphone sensors, we present a GNSS-to-VIO time synchronization method. Furthermore, an adaptive positioning mechanism is devised based on the presence or absence of GNSS signals, allowing indoor and outdoor environment to switch. The proposed method is validated by integrating smartphone VIO with both smartphone built-in GNSS and high-performance geodetic GNSS receivers. The test results of the complex outdoor environments demonstrate that the proposed method significantly enhances GNSS positioning performance. The positioning accuracy of the proposed method using geodetic GNSS receiver is superior to that using smartphone GNSS. In outdoor environments, our method enables positioning accuracy of smartphone to improve 29.4, 53.0 and 69.9 % in three directions compared to single point positioning. Indoor and outdoor tests verified that the proposed method can provide continuous and reliable positioning in alternating interrupted and restored GNSS signals. [ABSTRACT FROM AUTHOR]

The Single-frequency Single-epoch double-differenced baseline resolution technique of Global Positioning System (GPS) provides a good opportunity for monitoring the displacement or deflection behavior of bridges under different loading conditions in real-time. However, for single GPS, a high success rate baseline solution is difficult to achieve due to the lack of sufficient visible satellites and the low accuracy of float solutions. Galileo Satellite Navigation System (Galileo) has 14 medium earth orbit satellites (as of May 2018) that can be used to supplement GPS. The frequency bands of Galileo overlap with that of GPS on E1/L1 and E5a/L5, which is conducive to the combination of observations in integration positioning. Accordingly, Galileo augmenting GPS is an effective and necessary approach to improve the positioning availability and reliability. Moreover, using the baseline length constraint can improve the accuracy of float solutions, narrow the search space, and finally increase the success rate of ambiguity resolution and positioning. The single-frequency single-epoch double-differenced GPS/Galileo mathematical model with baseline length constraint is deduced in this paper. Two sets of GNSS real bridge data were used for further analysis on the improvement of GPS/Galileo with baseline length constraint when compared to single GPS. Finally, a Fast Fourier Transformation (FFT) algorithm was adopted for precisely detecting the local dominant frequencies of XB, YB, and ZB direction of the two stations. [ABSTRACT FROM AUTHOR]

The Quasi-Zenith Satellite System (QZSS) service area covers the Asia-Pacific region and there are four quasi-zenith satellites (QZS) in orbit with three QZS in operation until March 2018. The QZSS is not required to work in a stand-alone mode, but the system can be used to enhance the Global Positioning System (GPS) or Beidou Satellite Navigation System (BDS). The availability, position dilution of precision (PDOP), ambiguity dilution of precision (ADOP), and success rate of GPS/QZSS and BDS/QZSS under different cut-off elevation angles were compared based on a simulation. Two sets of actual QZSS data were processed and analyzed for single-frequency single-epoch (SFSE) positioning together with GPS/BDS data in this paper. Different combination forms were executed to evaluate the positioning performance of GPS/QZSS and BDS/QZSS for two baseline cases. The results indicate that QZSS is able to increase the SFSE PDOP, ADOP, and success rate of the baseline resolution and decrease the position error for GPS or BDS, especially for longer GPS baseline data. The more QZS are used, the better the enhancement effect. [ABSTRACT FROM AUTHOR]

Abstract With the Global Navigation Satellite System (GNSS) developing, the single-frequency single-epoch multiple GNSSs (multi-GNSS) relative positioning has become feasible. Since a larger number of the observed satellites make the instantaneous (single-epoch) positioning time-consuming, a proper satellite selection is necessary. Among the present methods, the satellite selection with a fixed high cut-off elevation angle (CEA) is least time-consuming. However, there is no criterion how large a fixed high CEA should be to achieve a high success rate and less time consumption. Besides, a fixed high CEA makes the number of visible satellites largely variable, which affects the success rate. Hence, a satellite selection strategy based on ambiguity dilution of precision (ADOP) is proposed. Firstly, the theoretical proof that the ADOP increases the least when removing satellites are all low-elevation-angle satellites is given, which is important to achieve the fast positioning with a high success rate. Then, the threshold β is calculated for a different number of satellites and a given ADOP. The satellites are selected based on their elevation angles from high to low until β of the selected satellites becomes smaller than the corresponding threshold; this method is called the extended floating CEA multi-GNSS (EF-multi-GNSS). The comparison of the single-frequency single-epoch positioning performance of the EF-multi-GNSS with the satellite selections based on a fixed low CEA (L-multi-GNSS) and a fixed high CEA (H-multi-GNSS) via the relative positioning experiments shows that: (1) the EF-multi-GNSS with a minimal number of satellites can achieve the fast positioning and a high success rate close to 100%. It can greatly reduce the time consumption of the L-multi-GNSS, by about 64.0%, by selecting 12.6 satellites of 23.4 satellites; (2) the floating CEA of EF-multi-GNSS eliminates the consideration how large a fixed high CEA should be, and a CEA larger than the fixed high CEA of the H-multi-GNSS makes it more suitable for different conditions. [ABSTRACT FROM AUTHOR]

Interferometric phase filtering represents an indispensable step in interferometric synthetic aperture radar (InSAR) data processing. However, conventional filtering methods fail to make a balance between the noise elimination and phase preserving in strong noise environments or dense fringe regions. This in turn leads to an inaccurate interferometric phase. To overcome this problem, the authors introduce the concept of slope‐compensated filter based on the local adaptive frequency estimation. This method uses the estimation of signal parameters via rotational invariance techniques (ESPRITs), based on the singular value decomposition of the correlation matrix and generalised eigenvalue solution for providing an accurate local fringe frequency. Meanwhile, the size of the estimation window is adaptively determined by the coherence coefficient, and the judgement and modification of the invalid frequency estimation are utilised to further improve the accuracy of the local fringe frequency. Moreover, the authors consider a two‐stage with the back projection technique to further eliminate the noise residual and improve the filtering performance. The simulated and TerraSAR‐X add‐on for Digital Elevation Measurements (TanDEM) data sets are used to verify the effectiveness of the proposed method. The experimental results show that this method is capable of efficiently reducing the noise level as well as minimising the loss of the signal, outperforming other conventional methods. [ABSTRACT FROM AUTHOR]