GNSS Outages Research Articles

Cooperative Elliptic Positioning Through Single UAV During GNSS Outages

Cooperative Elliptic Positioning Through Single UAV During GNSS Outages

Localization robustness improvement for an autonomous race car using multiple extended Kalman filters

In this paper, we introduce a vehicle localization method designed for the SZEnergy race car, which competes in the Shell Eco-marathon. The proposed method comprises four different extended Kalman filter-based localization algorithms and a selection algorithm that determines the most suitable one based on vehicle speed, GNSS availability, and signal quality. The low-speed Kalman filters are based on a kinematic vehicle model while the high-speed variants are based on a dynamic vehicle model. Several measurements were performed during test maneuvers to evaluate the performance of the filters. The proposed method succesfully handles sensor miscalibration and GNSS outages.

Error Compensation Method of GNSS/INS Integrated Navigation System Based on AT-LSTM During GNSS Outages

Error Compensation Method of GNSS/INS Integrated Navigation System Based on AT-LSTM During GNSS Outages

A sub-μs accuracy GPS alternative using electrical transmission grids as precision timing networks.

It is widely recognised that over-reliance on GNSS (e.g GPS) for time synchronisation represents an acute threat to modern society, and a diversity of alternatives are required to mitigate the threat of an outage. This paper proposes a GNSS alternative using time dissemination over national scale transmission or distribution networks. The method utilises the same frequency bandwidth and coupling technology as established power line carrier technology in conjunction with modern chirp Spread Spectrum modulation. The basis of the method is the transmission of a time synchronised chirp from a central substation, coupled into the aerial modes of the transmission line. During GNSS operation, all substations can estimate the time of flight by correlating the received chirp with a time-synchronised local copy. During GNSS outage, time sychronisation to the central substation is maintained by correcting for the precalculated time of flight. It is shown that recent advances in chirp spread spectrum allow for a computationally efficient algorithm with the capacity to compute hundreds of thousand of chirp correlations every second, facilitating timing accuracy which satisfies the majority of smart grid applications. ATP-EMTP simulations of the method on large transmission networks demonstrate sub-μs timing accuracy even in the presence of low SNR and impulsive noise. An FPGA prototype demonstrates experimentally sub-μs accuracy for time dissemination over a distance of 700 m. Averaging over time is shown to facilitate satisfactory performance down to , which could extend the range of the system to a national scale and a time dissemination network invulnerable to wireless spoofing and jamming attack vectors.

Aerodynamic modeling of a delta-wing UAV for model-based navigation

This study aims to identify an optimal, as well as practical, parametric structure for a delta-wing UAV aerodynamic model for the purpose of model-based navigation. We present a comprehensive procedure for characterizing the aerodynamics of this platform, utilizing a hybrid approach that combines open-air wind-tunnel experiments with the processing of real flight data using filter error method. The experimental design employs Latin Hypercube Sampling to maximize the observability of aerodynamic coefficients while adhering to time constraints. Candidate aerodynamic models are selected through step-wise regression. Numerical values for model coefficients are determined experimentally and subsequently calibrated through a two-phase procedure using real flight data. We then compare these models by assessing their effectiveness in improving navigation in the absence of GNSS signal in four different test flights, with respect to conventional inertial coasting using the autopilot IMU. The experimental evidence demonstrates that the model-based navigation, utilizing the proposed aerodynamic model structures, significantly reduces positioning errors compared to traditional navigation methods during GNSS outages.

A GNSS/INS Integrated Navigation Compensation Method Based on CNN–GRU + IRAKF Hybrid Model During GNSS Outages

A GNSS/INS Integrated Navigation Compensation Method Based on CNN–GRU + IRAKF Hybrid Model During GNSS Outages

A MEMS-INS/GNSS Integrated System With FM Radio Signal-Aided Distance Increment Estimation During GNSS Outages

A MEMS-INS/GNSS Integrated System With FM Radio Signal-Aided Distance Increment Estimation During GNSS Outages

A ROBUST AUTONOMOUS VEHICULAR NAVIGATION SYSTEM USING RIMU-BASED INS/GNSS INTEGRATED SCHEME

Abstract. The application of the INS/GNSS integrated system is widespread in vehicle navigation. However, in certain complex urban areas, achieving accurate positioning for autonomous vehicles at level 3 and beyond presents a challenge. Moreover, ensuring the continuous availability of the navigation system is crucial for maintaining vehicle safety under all circumstances. Consequently, the specifications of the Inertial Measurement Unit (IMU), which have a direct correlation with cost, dominate the performance of the navigation system in environments where GNSS signals are denied or challenging. To implement level 3 autonomous vehicles, a strategy based on redundant IMUs (RIMU) is employed to not only enhance accuracy and availability but reduce the overall cost of the navigation system. This research mainly focuses on the development of a robust autonomous vehicular navigation system by designing an integrated algorithm for the low-cost RIMU and GNSS receiver within the INS/GNSS integration scheme. The positioning performance is analyzed by using a reference system and a benchmark system in the open sky, GNSS challenging, and GNSS outage scenarios.

An Adaptive Integrated Positioning Method for Urban Vehicles Based on Multitask Heterogeneous Deep Learning During GNSS Outages

An Adaptive Integrated Positioning Method for Urban Vehicles Based on Multitask Heterogeneous Deep Learning During GNSS Outages

An Integrated INS/GNSS System With an Attention-Based Deep Network for Drones in GNSS Denied Environments

We propose a neural network-based approach to assist positioning in an integrated Inertial Navigation System/Navigation Satellite System (INS/GNSS) during GNSS interruptions. One can aid a navigation system during GNSS outages by substituting the GNSS measurements with a well-trained Neural Network (NN). Outputs of this NN can then be used in a Kalman filtering scheme to acquire the best consistent and concise estimates according to the INS measurements. Since this problem has inherent spatial and temporal aspects, the proposed NN should account for both aspects simultaneously. Convolutional Long Short-term Memory (CLSTM) structure is an excellent candidate to satisfy the aforementioned requirement. The designed CLSTM architecture uses the angular rates and specific forces measured by INS to output the pseudo GNSS position increment covering for the lost GNSS signal. An attention mechanism is added to the final layer of the CLSTM to counter the gradient vanishing problem in long time-series prediction (ACLSTM). A field test utilizing a fixed-wing Unmanned Aerial Vehicle (UAV) is arranged to evaluate the proposed architecture's performance. The field test result shows a significant 3-dimensional positioning improvement during GNSS outages.

PCDA™ SLAM-BASED TECHNOLOGY FOR POINT CLOUD AND TRAJECTORY OPTIMIZATION FOR AIRBORNE, LAND, AND INDOOR APPLICATIONS IN GNSS-DENIED ENVIRONMENTS

Abstract. This paper presents the results of assessing the performance of Trimble Applanix PCDA™ SLAM-based technology to simultaneously optimize any mobile mapping system trajectory and LiDAR point cloud data in a GNSS-denied environment. The simultaneous use of inertially-aided GNSS data along with LiDAR point clouds to optimally correct shifts and/or drifts in the trajectory in GNSS-denied environments is addressed in detail in this paper. A number of Trimble MX50 Mobile Mapping System data sets were acquired in Germany particularly to assess the performance of PCDA™. The land mobile mapping data sets were acquired in deep urban canyons which were purposely acquired that way to reach the most challenging land mobile mapping data sets in a GNSS-denied environment. The PCDA™ technology assessment results are presented in detail. In summary, the results show how LiDAR data can successfully be used to correct the trajectory shifts and drifts due to GNSS outages by simultaneously optimizing both point cloud and trajectory data.

Wheel Odometry with Deep Learning-Based Error Prediction Model for Vehicle Localization

Wheel odometry is a simple and low-cost localization technique that can be used for localization in GNSS-deprived environments; however, its measurement accuracy is affected by many factors, such as wheel slip, wear, and tire pressure changes, resulting in unpredictable and variable errors, which in turn affect positioning performance. To improve the localization performance of wheel odometry, this study developed a wheel odometry error prediction model based on a transformer neural network to learn the measurement uncertainty of wheel odometry and accurately predict the odometry error. Driving condition characteristics including features describing road types, road conditions, and vehicle driving operations were considered, and models both with and without driving condition characteristics were compared and analyzed. Tests were performed on a public dataset and an experimental vehicle. The experimental results demonstrate that the proposed model can predict the odometry error with higher accuracy, stability, and reliability than the LSTM and WhONet models under multiple challenging and longer GNSS outage driving conditions. At the same time, the transformer model’s overall performance can be improved in longer GNSS outage driving conditions by considering the driving condition characteristics. Tests on the experimental vehicle demonstrate the model’s generalization capability and the improved positioning performance of dead reckoning when using the proposed model. This study explored the possibility of applying a transformer model to wheel odometry and provides a new solution for using deep learning in localization.

Continuous Decimeter-Level Positioning in Urban Environments Using Multi-Frequency GPS/BDS/Galileo PPP/INS Tightly Coupled Integration

Time- and precision-critical applications, such as autonomous driving, have extremely rigorous standards for continuous high-precision positioning. The importance of precise point positioning (PPP) technology for self-navigation equipment is self-evident by delivering decimeter-/centimeter-level absolute position accuracy in a global coordinate framework. Nevertheless, the prolonged initialization period renders PPP almost too difficult to be widely used for time-critical applications in real transportation scenarios, for example, in city canyons and overpasses. We proposed a method to accomplish continuous decimeter-level positioning by leveraging triple-frequency GNSS observations with an Inertial Navigation System (INS) aiding in urban environments. In the proposed method, the inertial measurements and the original tri-frequency pseudorange and carrier phase measurements are tightly fused in an extended Kalman filter to obtain the optimal state estimate. Afterwards, with precise extra-wide-lane (EWL) and wide-lane (WL) Uncalibrated Phase Delay (UPD) products, the EWL and WL ambiguities can be resolved sequentially to implement instantaneous decimeter-level positioning. Exploiting the short-term high-precision characteristic of the INS, the continuity of the PPP solution can be ameliorated noticeably, and the ability of decimeter-level positioning can be maintained effortlessly throughout the navigation process. With land vehicle data collected, several experiments are undertaken to comprehensively assess the capability of the proposed system in urban scenarios, taking the solution obtained by a commercial post-processing software as the reference. The single-epoch decimeter-level position estimation can be captured instantaneously for WL ambiguity-fixed PPP solutions. Furthermore, for WL ambiguity-fixed PPP/INS-integrated solutions, the position estimation is better than 0.2 m for horizontal components and improved by 40–90% compared with that of the WL ambiguity-fixed PPP solutions. More importantly, the availability rate of positioning accuracy is better than 0.3 m in the horizontal direction and 0.5 m in the up direction reaching 93.91%, whereas it is only 53.78% for WL ambiguity-fixed PPP solutions. Overall, the WL ambiguity-fixed PPP/INS-integrated solutions have the favorable performance to maintain continuous decimeter-level positioning for self-navigation equipment, even if full GNSS outages are encountered.

An integrated INS/GNSS system with an attention-based hierarchical LSTM during GNSS outage

An integrated INS/GNSS system with an attention-based hierarchical LSTM during GNSS outage

Position and Attitude Determination in Urban Canyon with Tightly Coupled Sensor Fusion and a Prediction-Based GNSS Cycle Slip Detection Using Low-Cost Instruments.

We present a position and attitude estimation algorithm of moving platforms based on the tightly coupled sensor fusion of low-cost multi baseline GNSS, inertial, magnetic and barometric observations obtained by low-cost sensors and affordable dual-frequency GNSS receivers. The sensor fusion algorithm is realized by an Extended Kalman Filter and estimates the states including GNSS receiver inter-channel biases, integer ambiguities and non-GNSS receiver biases. Tightly coupled sensor fusion increases the reliability of the position and attitude solution in challenging environments such as urban canyons by utilizing the inertial observations in case of GNSS outage. Moreover, GNSS observations can be efficiently used to mitigate IMU sensor drifts. Standard GNSS cycle slips detection methods, such as the application of triple differences or linear combinations such as Melbourne-Wübbena combination and the phase ionospheric residual extended TurboEdit method. However, these techniques are not well suited for the localization in quickly changing environments such as urban canyons. We present a new method of tightly coupled sensor fusion supported by a prediction based cycle slip detection technique, applied to a GNSS setup using three antennas leading to multiple moving baselines on the platform. Thus, not only the GNSS signal properties but also the dynamics of the moving platform are considered in the cycle slip detection. The developed algorithm is tested in an open-sky validation measurement and two sets of measurement in an urban canyon area. The sensor fusion algorithm processes the data sets using the proposed prediction-based cycle slip method, the loss-of-lock indicator-based, and for comparison, the Melbourne-Wübbena and the TurboEdit cycle slip detection methods are also included. The obtained position and attitude estimation results are compared to the internal solution of raw data source GNSS receivers and to the observations of a high-accuracy GNSS/INS unit including a fiber optic gyro. The validation test confirms the proper cycle slip detection in an ideal environment. The more challenging urban canyon test results show the reliability and the accuracy of the proposed method. In the case of the second urban canyon test, the proposed method improved the integer ambiguity resolution success rate by 19% and these results show the lowest horizontal and vertical coordinate distortion in comparison of the linear combination and the loss-of-lock-based cycle slip methods.

Speed Sensor-Aided Navigation Filter for Robust Localization in GNSS-Denied Mining Environments

<h3>Abstract</h3> The automation of mining haulage vehicles has great potential in terms of safety and economy. The performance of autonomous vehicles depends largely on highly accurate vehicle state information. Deep mines are especially challenging, as satellite-based localization methods are reaching their limits. Therefore, we introduce a new navigation filter concept for the precise and robust localization of the haulage fleet that can handle temporary GNSS interruptions in deep open-pit mines. The multi-sensor navigation filter utilizes an inertial measurement unit and is aided by GNSS. We introduce a new optical speed sensor update within the tightly coupled unscented Kalman filter. The speed sensor measures the slip-free two-dimensional speed above ground. The filter was validated with an articulated dumper in a gravel pit. The new filter achieved a mean position error of 0.24 m during a test drive of 190 s with a simulated GNSS outage of 90 s.

Low-Cost IMU and Odometer Tightly Augmented PPP-B2b-Based Inter-Satellite Differenced PPP in Urban Environments

Since 23 June 2020, BDS-3 has been entirely operated and obtained the ability of global PNT (Positioning, Navigation, and Timing) services. Afterward, real-time Precise Point Positioning (PPP) service is available in China’s surrounding areas via BDS-3 PPP-B2b signal. However, such a real-time PPP service cannot maintain the high accuracy and continuity of positioning solutions in challenging environments, such as urban environments. For that, we carried out a model by integrating between-satellite single-differenced (BSSD) PPP, a low-cost Inertial Navigation System (INS), and an odometer via an extended Kalman filter. The performance of this integration model was assessed with vehicle-borne data. Results demonstrated that (1) the position RMS (Root Mean Square) of BSSD PPP are 64.33 cm, 53.47 cm, and 154.11 cm. Compared with BSSD PPP, about 31.2%, 23.3%, and 27.3% position improvements can be achieved by using INS. Further enhancements of position RMS benefiting from the odometer are 1.34%, 1.41%, and 1.73% in the three directions. (2) Anyway, the accuracy of BSSD PPP/INS/Odometer tightly coupled integration is slightly higher than that of undifferenced PPP/INS/Odometer integration, with average improvement percentages of 7.71%, 3.09%, and 0.27%. Meanwhile, the performance of BSSD PPP/INS/Odometer integration during the periods with satellite outages is better than the undifferenced PPP-based solutions. (3) The improvements in attitudes from an odometer are more significant on heading angle than the other two attitudes, with percentages of 25.00%. (4) During frequent GNSS outage periods, the reduction in average maximum position drifts provided by INS are 18.01%, 8.95%, and 20.74%. After integrating with an odometer, the drifts can be furtherly decreased by 25.11%, 15.96%, and 20.69%. For attitude, about 41.67% reduction in average maximum drifts of heading angles is obtained.

Accuracy and robustness of ODO/NHC measurement models for wheeled robot positioning

The odometer (ODO) and non-holonomic constraint (NHC) are disturbed to a greater extent for wheeled robots by more serious vibrations and bumping compared to commercial cars. However, there have been few studies regarding the performances of different ODO/NHC measurement models for wheeled robot GNSS/INS positioning. In this study, the distance increment model was applied to not only the ODO measurement but also the NHC constraint. The measurement accuracy and robustness of this proposed 3D distance increment measurement versus conventional velocity measurement models were theoretically analyzed, and field tests evaluated in terms of positioning accuracy and robustness for wheeled robots. In the short-term (i.e., 1 min) GNSS outage test, the forward, lateral and vertical positioning drifts of the distance increment model decreased by 67 %, 15 %, and 39 %, respectively; and it also demonstrated superior robustness in the cases of carrier vibration, emergency stops, and passing speed bumps.

An Integrated INS/LiDAR SLAM Navigation System for GNSS-Challenging Environments.

Traditional navigation systems rely on GNSS/inertial navigation system (INS) integration, in which the INS can provide reliable positioning during short GNSS outages. However, if the GNSS outage persists for prolonged periods of time, the performance of the system will be solely dependent on the INS, which can lead to a significant drift over time. As a result, the need to integrate additional onboard sensors is essential. This study proposes a robust loosely coupled (LC) integration between the INS and LiDAR simultaneous mapping and localization (SLAM) using an extended Kalman filter (EKF). The proposed integrated navigation system was tested for three different driving scenarios and environments using the raw KITTI dataset. The first scenario used the KITTI residential datasets, totaling 48 min, while the second case study considered the KITTI highway datasets, totaling 7 min. For both case studies, a complete absence of the GNSS signal was assumed for the whole trajectory of the vehicle in all drives. In contrast, the third case study considered the use of minimal assistance from GNSS, which mimics the intermittent receipt and loss of GNSS signals for different driving environments. The positioning results of the proposed INS/LiDAR SLAM integrated system outperformed the performance of the INS for the residential datasets with an average reduction in the root mean square error (RMSE) in the horizontal and up directions of 88% and 32%, respectively. For the highway datasets, the RMSE reductions were 70% and 0.2% for the horizontal and up directions, respectively.

GNSS/INS Integration Based on Machine Learning LightGBM Model for Vehicle Navigation

To solve the problem of data accuracy degradation of vehicle GNSS/INS integrated navigation systems when the GNSS signal is unavailable or there is a GNSS outage, this paper improves the existing GNSS/INS integration methodology for land vehicle navigation based on the AI method. First, a GNSS/INS integration methodology for land vehicle navigation based on position update architecture (PUA) using LightGBM regression for predicting the position of a vehicle during a GNSS outage is presented. It uses LightGBM to model the relationship between INS data and vehicle position changes. On-board INS and GNSS data are collected when the GNSS signal is available and are used to train the PUA-LightGBM model; in the event of a GNSS outage, INS data are used as the input to the PUA-LightGBM to predict the change in vehicle position. Second, a vehicle navigation data acquisition system was designed for model validation. This included a self-developed GNSS/INS integrated navigation system and a Novatel pwrpak7-e1 GNSS/INS integrated navigation system for data acquisition on six road segments. Finally, the collected data were used for machine learning training of the PUA-LightGBM model and the existing PUA-RandomForest model. As a result, the PUA-LightGBM predicts the vehicle position with less error in the event of a GNSS outage and takes less time to train. It was also demonstrated that by allowing the model to be dynamically trained or updated while the vehicle is moving the PUA-LightGBM could adapt perfectly to the predictions of vehicle position changes in different complex road segments.