Open-sky Environments Research Articles

The B-spline mapping function (BMF): representing anisotropic troposphere delays by a single self-consistent functional model

Troposphere’s asymmetry can introduce errors ranging from centimeters to decimeters at low elevation angles, which cannot be ignored in high-precision positioning technology and meteorological research. The traditional two-axis gradient model, which strongly relies on an open-sky environment of the receiver, exhibits misfits at low elevation angles due to their simplistic nature. In response, we propose a directional mapping function based on cyclic B-splines named B-spline mapping function (BMF). This model replaces the conventional approach, which is based on estimating Zenith Wet Delay and gradient parameters, by estimating only four parameters which enable a continuous characterization of the troposphere delay across any directions. A simulation test, based on a numerical weather model, was conducted to validate the superiority of cyclic B-spline functions in representing tropospheric asymmetry. Based on an extensive analysis, the performance of BMF was assessed within precise point positioning using data from 45 International GNSS Service stations across Europe and Africa. It is revealed that BMF improves the coordinate repeatability by approximately 10%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10\\%$$\\end{document} horizontally and about 5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$5\\%$$\\end{document} vertically. Such improvements are particularly pronounced under heavy rainfall conditions, where the improvement of 3-dimensional root mean square error reaches up to 13%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$13\\%$$\\end{document}.

Practical Limitations of Using the Tilt Compensation Function of the GNSS/IMU Receiver

The research in this paper is related to the accuracy of the tilt compensation function of the GNSS/IMU receivers, which were examined in an open sky environment. The purpose of the paper is to point out to geodesists the conditions and limitations of using GNSS/IMU technology in precise measurements to not jeopardize the coordinate’s accuracy. The environment in which the measurement is made affects the quality of the GNSS signal and can limit the visibility of the satellite, leading to larger errors in the measurement. In this experiment, the current performance of the GNSS/IMU receivers was checked. Seven GNSS/IMU receivers were used for the realization of the experiment. For six receivers the compensation angle was α = 30°, while for one receiver, the compensation angle was α = 45°. The standard uncertainty of GNSS coordinates of the antenna phase center has values less than 9 mm. The standard uncertainty of the IMU component has values less than 31 mm. The measurement uncertainty of the position of the used GNSS receivers is in the range of 18.1 mm to 31.7 mm. The limit values for the differences along the coordinate axes x and y were determined, and their values are from 26 mm to 44 mm. In the conducted experiment, it was confirmed that three GNSS/IMU receivers have a “Satisfactory” result. The results show that GNSS/IMU measurements with a slope greater than 30° significantly affect the accuracy and reliability of GNSS/IMU technology. A slope greater than 45° has a deviation along the coordinate axes of 121.3 mm. The conducted research is particularly important for geodetic works that require high positioning performance. The testing method of the GNSS/IMU receiver presented in this paper can help its users to make correct conclusions regarding the coordinate accuracy of the measured point of interest.

IMPROVING GNSS POSITIONING RELIABILITY AND ACCURACY BASED ON FACTOR GRAPH OPTIMIZATION IN URBAN ENVIRONMENT

Abstract. Global navigation satellite system (GNSS) can provide global, precise, and continuous positioning in open-sky environments. However, urban environments with frequent outliers and cycle slips degrade the traditional Extended Kalman Filter (EKF) positioning performance. The susceptibility of EKF to outliers is attributed to its inherent structure. To mitigate, the GNSS positioning based on the Factor Graph Optimization (FGO) structure is adopted. FGO can enhance time correlation among observations and enable the updating of historical information, thereby improving resistance against outliers. In this study, we proposed a single-differenced GNSS-FGO model instead of the double-differenced model to preserve the sparsity of FGO, and outlier detection and PAR methods are employed to ensure urban positioning performance. To evaluate the proposed structure, experiments are conducted in both urban and open-sky environments. The results demonstrate the improvement of positioning accuracy and reliability, compared to EKF.

LTE RSSI Based Vehicular Localization System in Long Tunnel Environment

Current navigation systems rely heavily on global navigation satellite systems (GNSSs), which provide stable positioning results in open-sky environments but exhibit severe performance degradation in areas where the signal is compromised, such as dense urban environments, underground parking lots, and tunnels. Current navigation systems use an initial entry velocity to estimate a vehicle's position in a tunnel. Therefore, when the speed of a vehicle changes, a corresponding error occurs with respect to the determined position of the vehicle. This study proposes a novel localization technology to estimate a vehicle's position using long-term evolution (LTE) signals measured using a smartphone in a tunnel. Many antennas are installed along the length of the tunnel to increase LTE coverage. Thus, the LTE received signal strength indicator (RSSI) forms a unique pattern with several peaks in the tunnel. The accumulated LTE RSSI pattern was used to estimate the vehicle position in the tunnel. After the LTE fingerprint inside the tunnel was constructed, the position with the highest correlation with the LTE RSSI sequence of the user buffer was determined as the current vehicle location. To demonstrate the feasibility of the proposed system, we conducted an extensive field test in an actual tunnel using various off-the-shelf smartphones. Experimental results show that the proposed technology provides stable performance throughout the tunnel space.

A Robust Android Gnss Rtk Positioning Scheme Using Factor Graph Optimization

The release of raw Android global navigation satellite system (GNSS) measurements makes high-precision positioning achievable with low-cost smart devices. Affected by low-cost GNSS chips, linearly polarized antennas, and complex observation environments, Android GNSS observations usually contain a large number of outliers, which significantly degrade their positioning precision and reliability. To address this issue, a robust real-time kinematic (RTK) scheme with sliding window-based factor graph optimization (FGO) was developed. The scheme adopts the GNSS carrier-phase sliding window marginalization, models the carrier-phase ambiguity as a random constant, and incorporates multiple robust estimation strategies. Vehicle kinematic positioning validations were carried out in both open-sky and complex urban environments using representative Xiaomi Mi8 and Huawei P40 smartphones. Using the proposed scheme, the root mean square (rms) of the positioning errors in the east, north, and up components in the open-sky environments is 0.18, 0.13, and 0.38 m, respectively. In complex urban environments, the rms of the positioning precisions in the east, north, and up components was as decent as 1.42, 1.97, and 2.63 m, respectively.

THE DEVELOPMENT AND VALIDATION OF A NAVIGATION GRADE EGI SYSTEM FOR LAND VEHICULAR NAVIGATION APPLICATIONS

Abstract. In recent years, most people use commercial integrated navigation systems to develop navigation algorithms. However, due to the different levels of sensors on the market, it is difficult to customize commercial systems and leads to limited development of navigation algorithms. Therefore, the purpose of this research is to develop a real-time integrated navigation system EGI-1000 (Embedded GNSS and INS) including software and hardware, and effectively reduce the cost with the commercial price. The real-time integrated navigation system EGI-1000 contains a navigation-grade IMU, IMU1000 and NovAtel OEM 7720 GNSS receiver module. In this research, the integration process can be divided into three parts. The first part is the integration of hardware, and the architecture diagram of the real-time integrated navigation system will be displayed. The second part is the pre-processing of data. In the multi-sensor time synchronization problem, this research will propose a method about cross-correlation to validate whether the timestamp of IMU data is delay. The last part is algorithm about fusing data from multiple sensors and motion constraints. Extended Kaman Filter (EKF) will be the core and motion constraints including Zero Velocity Update (ZUPT) and Non-Holonomic Constraints (NHC) are integrated in the Loosely Coupled (LC) scheme. The calibration of Inertial navigation Measurement Unit (IMU) will also be conducted to determine the parameter in algorithm. The results of the experiments will be shown in this paper. Both of hardware and navigation algorithm in the integrated navigation system of this research are used to conduct multiple experiment including open sky environments, GNSS challenging environments, and GNSS denied environment. In comparison with the reference data, the navigation accuracy of the developed integrated navigation system can achieve centimeter-level accuracy (“Active Control” level and “Where in lane” level) in open sky and GNSS challenging environments. According to the propagation error theory, the result in GNSS denied environment also meet the expected value. The navigation algorithm is also feasible for the commercial integrated navigation system.

Cosmic time calibrator for wireless sensor network

Time synchronization of sensor nodes is critical for optimal operation of wireless sensor networks (WSNs). Since clocks incorporated into each node tend to drift, recurrent corrections are required. Most of these correction schemes involve clients periodically receive RF timing signals from a time server. However, an RF-based scheme is prone to glitches or failure unless operating in a region with almost entirely unobstructed space; hence it only operates well in a limited range of environments. For example, GPS requires open-sky environments. Moreover, the precision of land-based RF schemes is limited to a few micro seconds. In this work, we report on a more versatile and new type of recurrent clock resynchronization scheme called cosmic time calibrator (CTC) and its development and testing. CTC utilizes cosmic-ray muon signals instead of RF signals. Muons are penetrative and continuously precipitating onto the Earth’s surface, and they tend to travel linearly through encountered matter at approximately the speed of light in vacuum. Therefore, muons themselves can periodically transfer the precise timing information from node to node; hence, the performance of the inter-nodal communication device such as Wi-Fi or Bluetooth is minimized/unnecessary for an online/offline WSN analysis. The experimental results have indicated that a resynchronization frequency and precision of 60 Hz and ± 4.3 ns (S.D.) can be achieved. Modelling work of the WSN-based structural health monitoring of aerospace structures has shown that CTC can contribute to the development of new critical and useful applications of WSN in a wider range of environments.

Performance Analysis of Real-Time GPS/Galileo Precise Point Positioning Integrated with Inertial Navigation System

The integration of global navigation satellite system (GNSS) precise point positioning (PPP) and inertial navigation system (INS) is widely used in navigation for its robustness and resilience, especially in case of GNSS signal blockage. With GNSS modernization, a variety of PPP models have been developed and studied, which has also led to various PPP/INS integration methods. In this study, we investigated the performance of a real-time GPS/Galileo zero-difference ionosphere-free (IF) PPP/INS integration with the application of uncombined bias products. This uncombined bias correction was independent of PPP modeling on the user side and also enabled carrier phase ambiguity resolution (AR). CNES (Centre National d'Etudes Spatiales) real-time orbit, clock, and uncombined bias products were used. Six positioning modes were evaluated, including PPP, PPP/INS loosely coupled integration (LCI), PPP/INS tightly coupled integration (TCI), and three of these with uncombined bias correction through a train positioning test in an open sky environment and two van positioning tests at a complex road and city center. All of the tests used a tactical-grade inertial measurement unit (IMU). In the train test, we found that ambiguity-float PPP had almost identical performance with LCI and TCI, which reached an accuracy of 8.5, 5.7, and 4.9 cm in the north (N), east (E) and up (U) direction, respectively. After AR, significant improvements on the east error component were achieved, which were 47%, 40%, and 38% for PPP-AR, PPP-AR/INS LCI, and PPP-AR/INS TCI, respectively. In the van tests, frequent signal interruptions due to bridges, vegetation, and city canyons make the IF AR difficult. TCI achieved the highest accuracies, which were 32, 29, and 41 cm for the N/E/U component, respectively, and also effectively eliminated the solution re-convergence in PPP.

Integrity monitoring scheme for undifferenced and uncombined multi-frequency multi-constellation PPP-RTK

The precise point positioning (PPP)-based real-time-kinematic (RTK) method attracts increasing attention from both academia and industry because of its potential for high accuracy positioning with a shorter convergence time compared to the traditional PPP. Besides high accuracy, integrity monitoring (IM) is indispensable for safety–critical real-time land vehicle and aviation applications. As the traditional advanced receiver autonomous integrity monitoring (ARAIM) method is designed for (smoothed) pseudorange-based positioning, the complexity of multi-frequency multi-constellation PPP-RTK using carrier phase measurements has not been given sufficient consideration. This study proposes an IM scheme for multi-frequency multi-constellation uncombined PPP-RTK applying the ARAIM theory, with a new comprehensive threat model to accommodate not only pseudorange measurements, but also carrier phase measurements, and other fault events arising from the network corrections that support PPP-RTK. Characteristics of different types of faults are analyzed with the aid of numerical experiments. In addition, the impact of ambiguity-fixed solutions on PPP-RTK integrity performance is investigated. The authors have also conducted case studies, including static and real-kinematic positioning experiments. Experiments have demonstrated that fast convergence in accuracy and the position error bounds, or protection levels, with a given integrity risk, in horizontal position components of PPP-RTK could be achieved. For the open sky environments on a highway, the protection levels estimated by PPP-RTK solutions have the potential to meet the alert limit requirement for road transportation using ambiguity-fixed PPP-RTK positioning under the assumption that the risks of wrong ambiguity fixing are very small and can be ignored.

Camera-Based Lane-Aided Multi-Information Integration for Land Vehicle Navigation

Accurate positioning, especially lateral positioning, is an essential requirement of autonomous driving. Although high accuracy vehicle positioning can be conducted in open-sky environments by global navigation satellite system (GNSS), the positioning accuracy cannot be guaranteed in GNSS-denied environments such as urban canyons and tunnels. To solve this issue, inertial navigation systems, vehicle speed sensors, and vehicle motion constraints are often fused to mitigate positioning errors. However, this integration cannot meet the decimeter-level lateral positioning accuracy of autonomous vehicles’ localization requirements. This article proposes a multi-information integration method aided by lane distance to further eliminate lateral error. In the proposed method, the lateral vehicle-to-lane distance measurements from camera-based systems, and the map-matching lane distance based on high-definition map were utilized to provide absolute lane distance measurement corrections in GNSS-denied environments. Field vehicular tests with low-cost integration systems were conducted to evaluate the navigation performance of the proposed multi-information integration method, and the results indicated that it is feasible for the proposed method to maintain continuous and reliable lateral positioning accuracy of better than 0.6 m under reliable lane line detection conditions in GNSS-denied environments.

Performance Evaluation of a Typical Low-Cost Multi-Frequency Multi-GNSS Device for Positioning and Navigation in Agriculture—Part 2: Dynamic Testing

Dynamic performance of a GNSS (Global Navigation Satellite System) positioning device (PD) is of interest to end users of satellite-based auto-guidance systems (AGS) for agricultural vehicles, especially when a low-cost PD is used. This study evaluated the overall dynamic performance in an agricultural environment for the PDs paired with multi-frequency multi-GNSS receivers and antennae, including low-cost and legacy ones. The dynamic performance was evaluated in terms of reacquisition time, heading accuracy, positioning accuracy, and guidance accuracy at short and medium baselines of RTK (Real Time Kinematic) positioning. The dynamic testing was conducted using an instrumented vehicle with test PDs to perform the test runs on agricultural fields and asphalt roads. The test results proved that the dynamic performance of the low-cost PD was not inferior to that of the legacy one and accurate enough to be used as a positioning sensor for the auto guidance of agricultural vehicles. It could reacquire an RTK-fix solution within 4 s after a 3-s GNSS signal blockage and achieve sub-degree-level accuracy of the heading measurement for agricultural vehicles traveling in an open-sky environment. Furthermore, the low-cost PD could obtain a 4-cm-level dynamic RTK positioning accuracy and agricultural vehicle guidance accuracy of less than 3 cm. The techniques and test results from this study provided additional guidelines for determining the overall dynamic performance of the GNSS PDs and AGSs on an agricultural tractor.

Investigation of the trade-off between the complexity of the accelerometer bias model and the state estimation accuracy in INS/GNSS integration

Abstract The integration of Inertial Navigation Systems and Global Navigation Satellite Systems (GNSS) represents the core navigation unit for mobile platforms in open sky environments. A realistic assessment of the accuracy of the navigation solution depends on the accurate modelling of inertial sensor errors. Sensor noise and biases contribute most to short-term navigation errors. For the latter, different models can be used, varying in complexity. This paper investigates how the use of two different models for the accelerometer bias affects the accuracy of the state estimate in an extended Kalman filter. For this purpose, the Allan variance technique is applied to a data sequence from a specific inertial sensor to identify and quantify the underlying noise processes. The estimated noise parameters are used to characterise a bias model for the accelerometers that in addition to the static bias model takes non-white noise processes of the inertial sensor under investigation into account. This detailed accelerometer bias model is compared to a classical modelling approach that only considers static biases. Both approaches are evaluated based on simulation studies for continuous and intermittent GNSS coverages. The results show no significant difference between the two modelling approaches in terms of horizontal position and attitude precision. Furthermore, the correctness of the accelerometer bias estimates is not significantly affected by the modelling approach. All in all, it can be concluded that a detailed bias model of the accelerometers does not outperform the classical modelling approach.

Smartphone-Based Positioning Augmented by BDSBAS Ionospheric Corrections

BeiDou Satellite Based Augmentation System (BDSBAS) can notably improve the GNSS L1 positioning performance by transmitting the orbit, clock and ionosphere corrections. Aside from terrestrial geodetic receivers, the mass-market smartphones also benefit from the augmentation service. In this paper, the smartphone-based GPS-L1 standard point positioning (SPP) augmented by BDSBAS corrections is analyzed using Huawei-P40 smartphone. The quality of BDSBAS-B1C ionospheric information is evaluated by ionospheric observables retrieved from the selected 20 stations. Compared to the BDS-3 global ionosphere model (BDGIM) and the Global Ionosphere Maps (GIM) of the International GNSS Service (IGS), the accuracy of BDSBAS ionospheric correction is about 28% better than BDGIM and 11% better than the IGS-GIM. Compared to the GPS-L1 SPP, the accuracy of BDSBAS augmented smartphone positioning increases by 27% in the horizontal component and 41% in vertical component in the open-sky environment, and 50% improvement in both directions in the second experiment where the smartphone connects with an external geodetic GNSS antenna to reduce the negative effects of multipath errors.

Integration of Multi-GNSS PPP-RTK/INS/Vision with a Cascading Kalman Filter for Vehicle Navigation in Urban Areas

Precise point positioning (PPP) has received much attention in recent years for its low cost, high accuracy, and global coverage. Nowadays, PPP with ambiguity resolution and atmospheric augmentation is widely regarded as PPP-RTK (real-time kinematic), which weakens the influence of the long convergence time in PPP and regional service coverage in RTK. However, PPP-RTK cannot work well in urban areas due to limitations of non-line-of-sight (NLOS) conditions. Inertial navigation systems (INS) and vision can realize continuous navigation but suffer from error accumulation. Accordingly, the integration model of multi-GNSS (global navigation satellite system) and PPP-RTK/INS/vision with a cascading Kalman filter and dynamic object removal model was proposed to improve the performance of vehicle navigation in urban areas. Two vehicular tests denoted T01 and T02 were conducted in urban areas to evaluate the navigation performance of the proposed model. T01 was conducted in a relatively open-sky environment and T02 was collected in a GNSS-challenged environment with many obstacles blocking the GNSS signals. The positioning results show that the dynamic object removal model can work well in T02. The results indicate that multi-GNSS PPP-RTK/INS/vision with a cascading Kalman filter can achieve a positioning accuracy of 0.08 m and 0.09 m for T01 in the horizontal and vertical directions and 0.83 m and 0.91 m for T02 in the horizontal and vertical directions, respectively. The accuracy of the velocity and attitude estimations is greatly improved by the introduction of vision.

Improved GNSS vector tracking loop to enhance the navigation performance of USV

The Global Navigation Satellite System (GNSS) is the most common source of location and heading information for unmanned surface vehicle (USV) in real time. However, in areas such as ports or bridges, the GNSS signal will be occluded or weakened. The carrier frequency and code frequency of scalar tracking (ST) in GNSS receiver fluctuates greatly, and even the tracking loop will lose lock. To enhance the navigation performance of USV's GNSS receiver, this paper proposes a framework that uses fixed-lag smoothing (FLS) to assist vector tracking (VT) and applies unscented Kalman filter in the navigation processor. The pseudo-range error and pseudo-range rate error are used as the measurements of navigation processor. FLS serves as a backward filter to enhance the tracking ability of the code loop. The field experiments show that the positioning error of the proposed method is reduced by 72.1% compared to the traditional VT method in the open-sky environments. When the signal is occluded, the positioning results of the traditional ST are dispersed, and also the traditional VT method displays a large error. Moreover, the proposed method is able to reduce the positioning error by 85.4%. The proposed GNSS loop tracking architecture and method can provide a possible solution for USV's navigation system development.

Integrity monitoring for kinematic precise point positioning in open-sky environments with improved computational performance

Positioning integrity monitoring (IM) is essential for liability- and safety-critical land applications such as road transport. IM methods such as solution separation apply multiple filters, which necessitates the use of computationally efficient algorithms in real-time applications. In this contribution, a new approach that significantly improves the computation time of the measurement update of the Kalman filter is presented, where only one matrix inversion is applied for all filters with measurement subsets. The fault detection and identification method and computation of the protection levels (PLs) are discussed. The computational improvement comes at the expense of a small increase in the PL. Test results for precise point positioning (PPP) with float ambiguities in an open-sky and suburban environment demonstrate the reduced computation time using the proposed approach compared to the traditional method, with 23%–42% improvement. The availability of IM for PPP, i.e. when the PL is less than a selected alert limit of 1.625 m, ranged between 92% and 99%, depending on the allowable integrity risk, tested at 10−5 and 10−6, and the observation environment.

GPR mapping with mobile mapping sensing and tracking technologies

Ground Penetrating Radar (GPR) is commonly used for detecting and locating underground utilities, as well as identifying water leakages and other buried objects without the need for excavation. To precisely depict the subsurface by GPR, an accurate geo-referencing technique on each GPR scan is also essential. Traditional geo-referencing of GPR scans requires a survey wheel to walk along a predetermined grid, or relies on the Global Navigational Satellite Systems (GNSS) to work under open sky environments where enough satellites are available (Böniger and Tronicke, 2010; Chang, 2016). In this study, we attempt to use a mobile mapping system (MMS) backpack guided by SLAM as the geo-referencing device for synchronizing GPR scans in areas with bad or no GNSS, and is free of control points. The study develops a method to synchronise the SLAM positioning and GPR measurements and then validate the proposed method's precision and accuracy with a site case. It was proved that the integrationed time and labour required in the traditional gridding are minimized for improving the GPR survey efficiency, facilitating the large-scale survey in dense urban areas where good GNSS coverage is unavailable.

Modeling and Prediction of Inter-System Bias for GPS/BDS-2/BDS-3 Combined Precision Point Positioning

The combination of Precision Point Positioning (PPP) with Multi-Global Navigation Satellite System (MultiGNSS), called MGPPP, can improve the positioning precision and shorten the convergence time more effectively than the combination of PPP with only the BeiDou Navigation Satellite System (BDS). However, the Inter-System Bias (ISB) measurement of Multi-GNSS, including the time system offset, the coordinate system difference, and the inter-system hardware delay bias, must be considered for Multi-GNSS data fusion processing. The detected ISB can be well modeled and predicted by using a quadratic model (QM), an autoregressive integrated moving average model (ARIMA), as well as the sliding window strategy (SW). In this study, the experimental results indicate that there is no apparent difference in the ISB between BDS-2 and BDS-3 observations if B1I/B3I signals are used. However, an obvious difference in ISB can be found between BDS-2 and BDS-3 observations if B1I/B3I and B1C/B2a signals are used. Meanwhile, the precision of the Predicted ISB (PISB) on the next day of all stations is about 0.1−0.6 ns. Besides, to effectively utilize the PISB, a new strategy for predicting the PISB for MGPPP is proposed. In the proposed strategy, the PISB is used by adding two virtual observation equations, and an adaptive factor is adopted to balance the contribution of the Observed ISB (OISB) and the PISB to the final estimations of ISB. To validate the effectiveness of the proposed method, some experimental schemes are designed and tested under different satellite availability conditions. The results indicate that in open sky environment, the selective utilization of the PISB achieves almost the same positioning precision of MGPPP as the direct utilization of the PISB, but the convergence time of MGPPP is reduced by 7.1% at most in the north (N), east (E), and up (U) components. In the blocked sky environment, the selective utilization of the PISB contributes to more significant improvement of the positioning precision and convergence time than that in the open sky environment. Compared with the direct utilization of the PISB, the selective utilization of the PISB improves the positioning precision and convergence time by 6.7% and 12.7% at most in the N, E, and U components, respectively.

Residual Attention Network-Based Confidence Estimation Algorithm for Non-Holonomic Constraint in GNSS/INS Integrated Navigation System

Nowadays, the availability of accurate vehicle position becomes more and more indispensable. The GNSS/INS (Global Navigation Satellite Systems/Inertial Navigation System) is currently the most widely-used integrated navigation scheme for land vehicles, which is capable of provide high-accuracy and continuous positioning results in the open-sky environments. However, under the GNSS-denied conditions, the existing GNSS/INS integrated system often fails to provide reliable positioning results due to various and nonlinear errors contained in the MEMS (Micro-Electro-Mechanical System) IMU (Inertial Measurement Unit) measurements. To improve the positioning accuracy during GNSS outage, deep learning has been introduced into the GNSS/INS integrated system in recent years. In this paper, we propose a residual attention network-based confidence (i.e., measurement noise covariance) estimation algorithm for non-holonomic constraint in GNSS/INS integrated navigation system, which adopts a residual attention network to dynamically estimate the noise covariance of the pseudo-observation (i.e., non-holonomic constraint) for optimal Kalman filtering (KF) fusion. To emphasize the more representative features with larger weights for accurate noise covariance estimation, we introduce an attention mechanism to automatically assign proper weights to the learned features according to their contributions. We evaluate our proposed method on three practical road datasets and compare it with other seven methods including the traditional KF, Pure INS, KF with three deep learning networks, K-means, and the Input-Delayed Neural Networks based method. Extensive experimental results demonstrate that our proposed RA-NHC bounds the errors associated with velocities and achieves reasonable accuracy improvement in position and velocity estimation.

Estimation of articulated angle in six-wheeled dump trucks using multiple GNSS receivers for autonomous driving

Due to the declining birthrate and aging population, the shortage of labor in the construction industry has become a serious problem, and increasing attention has been paid to automation of construction equipment. We focus on the automatic operation of articulated six-wheel dump trucks at construction sites. For the automatic operation of the dump trucks, it is important to estimate the position and the articulated angle of the dump trucks with high accuracy. In this study, we propose a method for estimating the state of a dump truck by using four global navigation satellite systems (GNSSs) installed on an articulated dump truck and a graph optimization method that utilizes the redundancy of multiple GNSSs. By adding real-time kinematic (RTK)-GNSS constraints and geometric constraints between the four antennas, the proposed method can robustly estimate the position and articulation angle even in environments where GNSS satellites are partially blocked. As a result of evaluating the accuracy of the proposed method through field tests, it was confirmed that the articulated angle could be estimated with an accuracy of 0.1 in an open-sky environment and 0.7 in a mountainous area simulating an elevation angle of 45 where GNSS satellites are blocked.