Search Results (28)

A uniformly distributed global ground network is essential for the accurate determination of GNSS orbit and clock parameters. However, achieving an ideal ground network is often difficult. When limited to a sparse or regional network of ground stations, the integration of LEO satellites can substantially enhance the accuracy of GNSS Precise Orbit Determination (POD). In practical processing, discontinuities with complicated gaps can occur in LEO attitude quaternions, particularly when working with a restricted observation network. This hampers the accuracy of determining GNSS/LEO integrated orbits. To address this, an investigation was conducted using data from seven LEO satellites, including those from Sentinel-3, GRACE-FO, and Swarm, to evaluate integrated POD performance under sparse or regional station conditions. Particular focus was placed on addressing attitude discontinuities. Four scenarios were analyzed, encompassing both continuous data availability and one-, two-, and three-hour interruptions after one hour of continuous data availability. The results showed that the proposed quaternion rotation matrix interpolation method is reliable for the integrated POD of GNSSs and LEOs with strict attitude control. Full article

The robust and high-precision estimation of position and attitude information using a combined global navigation satellite system/inertial navigation system (GNSS/INS) model is essential to a wide range of applications in intelligent driving and smart transportation. GNSS systems are susceptible to inaccuracies and signal interruptions in occluded environments, which lead to unreliable parameter estimations in GNSS/INS based on filter models. To address this issue, in this paper, a GNSS/INS combination model based on factor graph optimization (FGO) is investigated and the robustness of this optimization model is evaluated in comparison to the traditional extended Kalman filter (EKF) model and robust Kalman filter (RKF) model. In this paper, both high- and low-accuracy GNSS/INS combination data are used and the two sets of urban scene data are collected using high- and low-precision consumer-grade inertial guidance systems and an in-vehicle setup. The experimental results demonstrate that the position, velocity, and attitude estimates obtained using the GNSS/INS and the FGO model are superior to those obtained using the traditional EKF and robust EKF methods. In the simulated scenarios involving gross interference and GNSS signal loss, the FGO model achieves optimal results. The maximum improvement rates of the position, velocity, and attitude estimates are 81.1%, 73.8%, and 75.1% compared to the EKF method and 79.8%, 72.1%, and 57.1% compared to the RKF method, respectively. Full article

Accurate and robust on-board orbit determination is essential for enabling autonomous spacecraft operations, particularly in scenarios where ground control is limited or unavailable. This paper presents a novel method for achieving robust on-board orbit determination by integrating a loosely coupled GNSS/INS architecture with an on-board orbit propagator through error Kalman filtering. This method is designed to continuously estimate and propagate a spacecraft’s orbital state, leveraging real-time sensor measurements from a global navigation satellite system (GNSS) receiver and an inertial navigation system (INS). The key advantage of the proposed approach lies in its ability to maintain orbit determination integrity even during GNSS signal outages or sensor failures. During such events, the on-board orbit propagator seamlessly continues to predict the spacecraft’s trajectory using the last known state information and the error estimates from the Kalman filter, which were adapted here to handle synthetic propagated measurements. The effectiveness and robustness of the method are demonstrated through comprehensive simulation studies under various operational scenarios, including simulated GNSS signal interruptions and sensor anomalies. Full article

In farmland environments where GNSS signals are obstructed, such as forested areas or in adverse weather conditions, traditional GNSS/INS integrated navigation systems suffer from positioning errors and instability. To address this, a model-assisted integrated navigation system is proposed, combining Convolutional Neural Networks (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) networks. The CNN-BiLSTM model is trained under normal GNSS conditions and used to predict positioning when GNSS signals are interrupted, effectively replacing GNSS to ensure stable and accurate navigation. Experimental validation is conducted using field data from tractor simulations. The results show that, during a 100-s GNSS denial, the CNN-BiLSTM model reduces the average position error by 79.3% compared to pure inertial navigation and by 5.4% compared to traditional LSTM. In a 30-s GNSS denial, the average position error is reduced by 41% compared to inertial navigation and 6.2% compared to LSTM. The model maintains positioning accuracy within 3% of the GNSS/INS output under normal conditions, demonstrating its feasibility and effectiveness. This approach offers a promising solution for autonomous tractor navigation in GNSS-denied agricultural environments, contributing to precision agriculture. Full article

In the field of navigation and positioning, the inertial navigation system (INS)/global navigation satellite system (GNSS) integrated navigation system is known for providing stable and high-precision navigation services for vehicles. However, in extreme scenarios where GNSS navigation data are completely interrupted, the positioning accuracy of these integrated systems declines sharply. While there has been considerable research into using neural networks to replace the GNSS signal output during such interruptions, these approaches often lack targeted modeling of sensor information, resulting in poor navigation stability. In this study, we propose an integrated navigation system assisted by a novel neural network: an inverted-Transformer (iTransformer) and the application of a frequency-enhanced channel attention mechanism (FECAM) to enhance its performance, called an INS/FECAM-iTransformer integrated navigation system. The key advantage of this system lies in its ability to simultaneously extract features from both the time and frequency domains and capture the variable correlations among multi-channel measurements, thereby enhancing the modeling capabilities for sensor data. In the experimental part, a public dataset and a private dataset are used for testing. The best experimental results show that compared to a pure INS inertial navigation system, the position error of the INS/FECAM-iTransformer integrated navigation system reduces by up to 99.9%. Compared to the INS/LSTM (long short-term memory) and INS/GRU (gated recurrent unit) integrated navigation systems, the position error of the proposed method decreases by up to 82.4% and 78.2%, respectively. The proposed approach offers significantly higher navigation accuracy and stability. Full article

In complex urban road environments, vehicles inevitably experience frequent or sustained interruptions of the Global Navigation Satellite System (GNSS) signal when passing through overpasses, near tall buildings, and through tunnels. This results in the reduced accuracy and robustness of the GNSS/Inertial Navigation System (INS) integrated navigation systems. To improve the performance of GNSS and INS integrated navigation systems in complex environments, particularly during GNSS outages, we propose a convolutional neural network–gated recurrent unit (CNN-GRU)-assisted factor graph hybrid navigation method. This method effectively combines the spatial feature extraction capability of CNN, the temporal dynamic processing capability of GRU, and the data fusion strength of a factor graph, thereby better addressing the impact of GNSS outages on GNSS/INS integrated navigation. When GNSS signals are strong, the factor graph algorithm integrates GNSS/INS navigation information and trains the CNN-GRU assisted prediction model using INS velocity, acceleration, angular velocity, and GNSS position increment data. During GNSS outages, the trained CNN-GRU assisted prediction model forecasts pseudo GNSS observations, which are then integrated with INS calculations to achieve integrated navigation. To validate the performance and effectiveness of the proposed method, we conducted real road tests in environments with frequent and sustained GNSS interruptions. Experimental results demonstrate that the proposed method provides higher accuracy and continuous navigation outcomes in environments with frequent and sustained GNSS interruptions, compared to traditional GNSS/INS factor graph integrated navigation methods and long short-term memory (LSTM)-assisted GNSS/INS factor graph navigation methods. Full article

In urban road environments, global navigation satellite system (GNSS) signals may be interrupted due to occlusion by buildings and obstacles, resulting in reduced accuracy and discontinuity of combined GNSS/inertial navigation system (INS) positioning. Improving the accuracy and robustness of combined GNSS/INS positioning systems for land vehicles in the presence of GNSS interruptions is a challenging task. The main objective of this paper is to develop a method for predicting GNSS information during GNSS outages based on a long short-term memory (LSTM) neural network to assist in factor graph-based combined GNSS/INS localization, which can provide a reliable combined localization solution during GNSS signal outages. In an environment with good GNSS signals, a factor graph fusion algorithm is used for data fusion of the combined positioning system, and an LSTM neural network prediction model is trained, and model parameters are determined using the INS velocity, inertial measurement unit (IMU) output, and GNSS position incremental data. In an environment with interrupted GNSS signals, the LSTM model is used to predict the GNSS positional increments and generate the pseudo-GNSS information and the solved results of INS for combined localization. In order to verify the performance and effectiveness of the proposed method, we conducted real-world road test experiments on land vehicles installed with GNSS receivers and inertial sensors. The experimental results show that, compared with the traditional combined GNSS/INS factor graph localization method, the proposed method can provide more accurate and robust localization results even in environments with frequent GNSS signal loss. Full article

Considering the problem of GNSS service interruption caused by the insufficient number of available satellites in complex environments, Low Earth Orbit (LEO) satellites can supplement GNSS effectively. To eliminate the unknown satellite clock error and the atmospheric delay error with spatial correlation in LEO observations, a Differential-Low Earth Orbit opportunistically enhancing GNSS (D-LoeGNSS) navigation framework is proposed. Firstly, because of the uncertainty of the LEO orbit, we derive the effect of the LEO orbit error on the differential measurement model. Secondly, aiming at the noise amplification and correlation in double-difference (DD), we propose a Householder-Based D-LoeGNSS (HB-DLG) algorithm, which suppresses noise by introducing an orthogonal matrix. Thirdly, in D-LoeGNSS, the typical measurement of LEO is Doppler, which is heterogeneous with the GNSS pseudorange, rendering the Dilution of Precision (DOP) evaluation method unsuitable. Given the unbiasedness of differential measurements, the Cramer Rao Lower Bound (CRLB) is derived as a metric to characterize the positioning accuracy and satellite spatial distribution. Finally, a field experiment using Orbcomm (ORB) and GPS is conducted. The experimental results show that the performance of the HB-DLG algorithm is superior to DD. Especially when the number of satellites is insufficient or the measurement redundancy is poor; the D-LoeGNSS framework has advantages of rapid convergence and high accuracy compared with a single constellation. Full article

The employment of unmanned aerial vehicles (UAVs) has greatly facilitated the lives of humans. Due to the mass manufacturing of consumer unmanned aerial vehicles and the support of related scientific research, it can now be used in lighting shows, jungle search-and-rescues, topographical mapping, disaster monitoring, and sports event broadcasting, among many other disciplines. Some applications have stricter requirements for the autonomous positioning capability of UAV clusters, requiring its positioning precision to be within the cognitive range of a human or machine. Global Navigation Satellite System (GNSS) is currently the only method that can be applied directly and consistently to UAV positioning. Even with dependable GNSS, large-scale clustering of drones might fail, resulting in drone cluster bombardment. As a type of passive sensor, the visual sensor has a compact size, a low cost, a wealth of information, strong positional autonomy and reliability, and high positioning accuracy. This automated navigation technology is ideal for drone swarms. The application of vision sensors in the collaborative task of multiple UAVs can effectively avoid navigation interruption or precision deficiency caused by factors such as field-of-view obstruction or flight height limitation of a single UAV sensor and achieve large-area group positioning and navigation in complex environments. This paper examines collaborative visual positioning among multiple UAVs (UAV autonomous positioning and navigation, distributed collaborative measurement fusion under cluster dynamic topology, and group navigation based on active behavior control and distributed fusion of multi-source dynamic sensing information). Current research constraints are compared and appraised, and the most pressing issues to be addressed in the future are anticipated and researched. Through analysis and discussion, it has been concluded that the integrated employment of the aforementioned methodologies aids in enhancing the cooperative positioning and navigation capabilities of multiple UAVs during GNSS denial. Full article

To provide continuous and reliable real−time precise positioning services in challenging environments and poor internet conditions, the real−time precise corrections of the BeiDou global navigation satellite system (BDS−3) PPP−B2b signal are utilized to correct the satellite orbit errors and clock offsets. In addition to this, using the complementary characteristics of the inertial navigation system (INS) and the global navigation satellite system (GNSS), a PPP−B2b/INS tight integration model is established. With observation data collected in an urban environment, the results show that PPP−B2b/INS tight integration can ensure a decimeter−level positioning accuracy; the positioning accuracies of the E, N, and U components are 0.292, 0.115, and 0.155 m, respectively, which can provide a continuous and secure position during short interruptions in the GNSS. However, there is still a gap of about 1 dm compared with the three−dimensional (3D) positioning accuracy obtained from Deutsche GeoForschungsZentrum (GFZ) real−time products, and a gap of about 2 dm compared with the GFZ post−precise products. Using a tactical inertial measurement unit (IMU), the velocimetry accuracies of the tightly integrated PPP−B2b/INS in the E, N, and U components are all about 0.3 cm/s, and the attitude accuracy of yaw is about 0.1 deg, while the pitch and roll show a superior performance of less than 0.01 deg. The accuracies of the velocity and attitude mainly depend on the performance of the IMU in the tight integration mode, and there is no significant difference between using real−time products and post products. The performance of the microelectromechanical system (MEMS) IMU and tactical IMU is also compared, and the positioning, velocimetry, and attitude determinations with the MEMS IMU are significantly worsened. Full article

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. Full article

Global navigation satellite system (GNSS) signals are easily blocked by urban canyons, tree-lined roads, and overpasses in urban environments, making it impossible to ensure continuous and reliable positioning using only GNSS, even with the widely used precise point positioning and real-time kinematic (PPP-RTK). Since the inertial navigation system (INS) and GNSS are complementary, a tightly coupled PPP-RTK/INS model is developed to improve the positioning performance in these GNSS-challenged scenarios, in which the atmospheric corrections are used to achieve a rapid ambiguity resolution and the mechanization results from INS are utilized to assist GNSS preprocessing, re-fixing, and reconvergence. The experiment was conducted using three sets of vehicle-mounted data, and the performance of low-cost receiver and microelectromechanical system (MEMS) inertial measurement unit (IMU) was compared. The result shows that the positioning accuracy of PPP-RTK/INS can reach 2 cm in the horizontal component and 5 cm in the vertical component in the open environment. In the complex urban environment, continuous and reliable positioning can be ensured during GNSS short interruption, ambiguity can be instantaneously re-fixed with the assistance of INS, and decimeter-level positioning accuracy can be achieved. As a result, the horizontal positioning errors of more than 95% of the total epochs were within 20 cm. In addition, average positioning accuracy better than 15 cm and 30 cm in the horizontal and vertical components, respectively, can be obtained using the low-cost receiver and MEMS IMU. Compared with tactical IMU, the improvements in positioning accuracy and the ambiguity fixing rate using the geodetic receiver were more significant. Full article

Vision-based unmanned aerial vehicle (UAV) localization is capable of providing real-time coordinates independently during GNSS interruption, which is important in security, agriculture, industrial mapping, and other fields. owever, there are problems with shadows, the tiny size of targets, interfering objects, and motion blurred edges in aerial images captured by UAVs. Therefore, a multi-order Siamese region proposal network (M-O SiamRPN) with weight adaptive joint multiple intersection over union (MIoU) loss function is proposed to overcome the above limitations. The normalized covariance of 2-O information based on1-O features is introduced in the Siamese convolutional neural network to improve the representation and sensitivity of the network to edges. We innovatively propose a spatial continuity criterion to select 1-O features with richer local details for the calculation of 2-O information, to ensure the effectiveness of M-O features. To reduce the effect of unavoidable positive and negative sample imbalance in target detection, weight adaptive coefficients were designed to automatically modify the penalty factor of cross-entropy loss. Moreover, the MIoU was constructed to constrain the anchor box regression from multiple perspectives. In addition, we proposed an improved Wallis shadow automatic compensation method to pre-process aerial images, providing the basis for subsequent image matching procedures. We also built a consumer-grade UAV acquisition platform to construct an aerial image dataset for experimental validation. The results show that our framework achieved excellent performance for each quantitative and qualitative metric, with the highest precision being 0.979 and a success rate of 0.732. Full article

Global Navigation Satellite System (GNSS) positioning technology has become the most effective method for real-time three-dimensional landslide monitoring. However, the GNSS observation signal is easily affected by the presence of a complex landslide environment with high occlusion and strong interference, in which case its accuracy and reliability cannot meet the requirements of landslide deformation monitoring. Although the accelerometers have strong autonomous working capacities and can complement the GNSS landslide monitoring technology, regular GNSS/accelerometer coupled deformation monitoring technology relies on high-quality GNSS measurement information in order to obtain high-precision accelerometer-reckoned results, derived by accurately estimating the baseline shift error (BSE). When the GNSS signal suffers severe interference, the GNSS monitoring error will be partially absorbed by the BSE component after Kalman filtering, resulting in the divergence of the deformation solution. In this study, an abnormal observation variance inflation model was used to process the simulated landslide monitoring data (GNSS and accelerometer raw observation) for three typical scenes—GNSS signal normally locked, signal partially lost, and short-term interruption. The results were as follows: (1) When the GNSS signal was normally locked, the accuracy was comparable to that of the coupled solution employing an accelerometer (the Root Mean Square (RMS) values in the East (E), North (N) and Upward (U) directions were 0.11 cm, 0.33 cm, and 0.30 cm, respectively). (2) When the GNSS signal was partially lost, the accelerometer could effectively suppress the low-precision float solution of the GNSS, but here, the accuracy of the coupled solution would also decrease with the duration of the floats (the RMS values were E—1.21 cm, N—0.31 cm, and U: 0.58—cm, respectively, when the floats lasted for 10 s, and increased to E—3.09 cm, N—0.39 cm, and U—1.14 cm when they lasted for 20 s, wherein E was the main simulated sliding direction). (3) When the GNSS signal was interrupted for a short time, the accuracy of the coupled solution gradually decreased during continuous interruption, and decreased more quickly during the sliding period of the landslide (when the interruption persisted for 10 s, the RMS values in the simulated landslide stability period were E—0.61 cm, N—0.24 cm, and U—0.25 cm, respectively, while in the simulated landslide sliding period they reached E—4.10 cm, N—6.84 cm, and U—2.30 cm). However, raw observations of the accelerometer could still effectively be used to assist in identifying the real state of the landslide, thereby providing auxiliary information pertinent to early landslide disaster warning. Full article

The BeiDou global navigation satellite system (BDS-3) has been officially providing a real-time precise point positioning (PPP) augmentation service, known as the PPP-B2b service, since 2020. Decimeter-level positioning accuracy is expected to be achieved based on the PPP-B2b service. It shows great potential for global navigation satellite system (GNSS) real-time applications, including, for example, vehicle positioning on land. However, the application of the PPP-B2b service is still full of challenges in the urban environment because of GNSS signal blockage. The inertial navigation system (INS) is a popular technology which can provide continuous positions under GNSS challenging scenarios. In this study, we constructed a BDS-3 PPP-B2b/INS loosely coupled integration system for vehicle positioning and evaluated its performance through two automotive experiments. In the first experiment, four periods of 30 s GNSS outages were simulated to evaluate the performance of PPP-B2b/INS loosely coupled integration during GNSS outages. During the simulated GNSS outages, PPP-B2b positioning did not work. Nevertheless, PPP-B2b/INS loosely coupled integration provided continues solution through INS mechanization. The averaged positioning errors at the last epoch of outages were 300.6/498.0/41.0 cm for PPP-B2b/MEMS-IMU and 18.6/21.8/6.1 cm for PPP-B2b/Tactical-IMU loosely coupled integration, in the east, north and up directions, respectively. In the second experiment, we drove the land vehicle in a complex urban environment for 15 min. During this period, two GNSS signal interruptions occurred due to the occlusion of bridges, lasting 15 s and 5 s, respectively. The results show that the improvement of positioning accuracy in the east, north, and up components were 64.1%, 77.8%, and 73.8% respectively for PPP-B2b/MEMS-IMU loosely coupled integration, and 63.9%, 79.5%, and 74.4% respectively for PPP-B2b/Tactical-IMU loosely coupled integration, as compared to the positioning accuracy of PPP-B2b only. Full article