Hasil untuk "Canals and inland navigation. Waterways"

Cheng-Wei Wang, Shau-Shiun Jan

Precise point positioning (PPP), which is characterized by reliable positioning accuracy and flexibility, has been regarded as a highly promising technique. Precise ephemeris is essential for PPP; however, the conventionally used standard product 3 components have an almost biweekly latency. The multi-global navigation satellite system (GNSS) advanced demonstration tool for orbit and clock analysis (MADOCA), a novel next-generation service, aims to provide real-time correction messages for rapid-convergence PPP in regional areas. Additionally, to ensure seamless navigation during signal-interrupted conditions, an inertial measurement unit (IMU) can be tightly integrated with the motion constraint models. This paper presents a comprehensive analysis of standalone MADOCA-PPP and MADOCA-enhanced tightly coupled PPP/IMU. The approaches were evaluated under multiple scenarios. In suburban regions, the horizontal root mean square error (RMSE) was 0.4 m, with a 95th percentile horizontal error of 0.6 m. In GNSS-challenging environments, the horizontal RMSE was 0.92 m, with a 95th percentile horizontal error of 1.6 m.

Prasenjit Sengupta

This paper develops a navigation accuracy metric for magnetic navigation when adapted to civil aviation. Metrics that are currently used may not directly apply to magnetic navigation because the assumptions behind these metrics are based on the statistics of more conventional navigation modalities where the uncertainty distribution in the horizontal plane is typically radially symmetric. Magnetic navigation challenges these assumptions. New standard deviation limits based on the probability of exceedance of radial error are derived. Use is made of quadratic forms over random variables, specifically the Hoyt distribution and its associated probability density function and cumulative density function. Although the density functions lack closed-form solutions, new approximations for this distribution are utilized in order to make rapid computation possible, enabling their use for navigation purposes. When used in conjunction with robust numerical methods, the new approach accurately calculates the bounds on error distributions that would meet the requirements associated with Performance Based Navigation.

Amir Allahvirdi-Zadeh, Ahmed El-Mowafy, Kan Wang

This paper investigates the potential of signals of opportunity for positioning using broadband low Earth orbit constellations. We developed analytical absolute and differential models based on Doppler-shift observations from multi-constellation satellite bursts across various frequency ranges. Owing to the unavailability of multi-constellation broadband receivers, simulations were conducted with the application of two primary restrictions common for these satellites: a 30° elevation mask angle and a 15-s intermittency for observations. Signal attenuation factors were modeled, indicating that free space loss was the dominant factor whereas cloud and fog losses were minimal. The accuracy of absolute static positioning, considering the aforementioned broadband restrictions, reached 4.32 m. The kinematic receiver showed similar trends, with a degraded accuracy of 4.83 m. Tests in urban areas revealed significant accuracy degradation to approximately 10 m. However, the differential model significantly improved kinematic positioning accuracy, achieving promising sub-meter levels even with a limited number of satellites.

Dongchan Min, Noah Minchan Kim, Gihun Nam et al.

Carrier-phase differential global navigation satellite systems (CDGNSSs) present an attractive option for autonomous vehicles that require accurate and safe navigation. The key to high precision in a CDGNSS is resolving integer ambiguities. However, the discrete nature of ambiguities complicates the analysis of position errors in relation to satellite measurement faults, which poses challenges in protection level (PL) calculation. This paper presents an integrity architecture based on solution separation receiver autonomous integrity monitoring. The test statistic for this monitor is defined in the position domain, directly capturing position errors due to faults. This approach facilitates easier and less conservative evaluations of PLs. This paper provides detailed derivations of PLs and monitor thresholds starting from a common definition of integrity and continuity risk. Additionally, this work presents a method for ensuring that PLs reliably bound actual position errors using a measurement overbounding technique. Simulation results show that the monitor detects most faults and that the PLs bound the position errors from undetected faults.

Michael C. Esswein, Mark L. Psiaki

New optimization-based methods have been developed to use measured direction-of-arrival (DoA) information in order to classify received global navigation satellite system signals into authenticated and spoofed sets and to augment that information with pseudorange information when DoA information alone is insufficient to achieve the needed classification. These methods are designed for a system that is being developed to mitigate spoofing and jamming by using signals from a controlled radiation pattern antenna. These new spoofing classification methods operate on DoA outputs from trackers of various signals. This paper presents a multi-hypothesis test that considers all possible hypotheses regarding the authenticated and spoofed sets of tracked signals. A combinatorial analysis is performed in which all possible authenticated-set/spoofed-set classifications are generated for a given set of tracked signals and the correct authenticated set is determined among the different combinations. Results from Monte Carlo simulations show that using a combined DoA and pseudorange method is suitable for determining the correct combinations.

P. Negi, R. Kromanis, A. Dorée et al.

Inland navigation structures (INS) facilitate transportation of goods in rivers and canals. Transportation of goods over waterways is more energy efficient than on roads and railways. INS, similar to other civil structures, are aging and require frequent condition assessment and maintenance. Countries, in which INS are important to their economies, such as the Netherlands and the United States, allocate significant budgets for maintenance and renovation of exiting INS, as well as for building new structures. Timely maintenance and early detection of a change to material or geometric properties (i.e., damage) can be supported with the structural health monitoring (SHM), in which monitored data, such as load, structural response, environmental actions, are analyzed. Huge scientific efforts are realized in bridge SHM, but when it comes to SHM of INS, the efforts are significantly lower. Therefore, the SHM community has opportunities to develop new solutions for SHM of INS and convince asset owners of their benefits. This review article, first, articulates the need to keep INS safe to use and fit for purpose, and the challenges associated with it. Second, it defines and reviews sensors, sensing technologies, and approaches for SHM of INS. Then, INS and their components, including structures in ports, are identified, described, and illustrated, and their monitoring efforts are reviewed. Finally, the review article emphasizes the added value of SHM systems for INS, concludes on the current achievements, and proposes future trajectories for SHM of INS and ports.

Luca Cucchi, Sophie Damy, Ciro Gioia et al.

With corrections transmitted through the E6 signal, the Galileo High Accuracy Service (HAS) provides the information necessary to execute a stand-alone precise point positioning algorithm in real time. Once fully operational, the service aims to deliver an accuracy of 20 cm and 40 cm (at the 95% confidence level) in the horizontal and vertical channels, respectively. While most of the current literature focuses on analyzing the performance of HAS in static and open-sky signal reception scenarios, this study presents the results of tests conducted in both static and dynamic conditions, including open-sky and urban canyon scenarios. The tests clearly demonstrate that utilizing HAS corrections leads to a significant reduction in positioning error across all tested environments. Furthermore, a specific analysis of HAS message availability in a harsh environment indicates that the corrections obtained from the signal in space are available approximately 95% of the time during dynamic scenario tests.

Shaolong Zheng, Kungan Zeng, Zhenni Li et al.

Signals from global navigation satellite systems (GNSSs) in urban areas suffer from serious multipath errors caused by building blockages and reflections. The use of deep neural networks offers great potential for predicting and eliminating complex multipath/non-line-of-sight (NLOS) errors. However, existing methods for predicting the original signals face two remaining challenges. The first challenge is an inability to effectively exploit irregular GNSS measurement data caused by an inconsistent number of visible satellites in different epochs. The second challenge is degradation in the generalization performance of the multipath/NLOS prediction model when using data collected from different locations and periods. To address these challenges, this paper proposes a novel graph transformer neural network (GTNN) for predicting satellite visibility that effectively learns environment representations from irregular GNSS measurements to both alleviate multipath interference and improve the generalization performance of the multipath prediction model. To learn from irregular GNSS measurements, a sky satellite graph is constructed as input to a graph neural network by using satellites captured in the same epoch, which can represent the spatial relationships between satellites and enable the model to learn satellite-related features sufficiently well. To improve the generalization ability of our multipath prediction model, a multihead attention mechanism is introduced to aggregate satellite node information by computing the correlation between satellites to extract the environment representation around the receiver. Based on the constructed sky satellite graph and the multihead attention mechanism, our novel GTNN for predicting satellite visibility can not only handle irregular GNSS measurements but can also learn an environment representation via graph attention. Comparative experiments were conducted on real-world GNSS measurement data in urban areas, demonstrating that the proposed method can achieve an accuracy exceeding 96% for satellite visibility prediction and obtain better generalization performance than existing multipath prediction methods. Moreover, the attention weights among satellites were visualized to demonstrate the environment representation learned by the GTNN from the sky satellite graph.

Matthew McDermott, Jason Rife

Distribution-to-distribution point cloud registration algorithms are fast and interpretable and perform well in unstructured environments. Unfortunately, existing strategies for predicting the solution error for these methods are overly optimistic, particularly in regions containing large or extended physical objects. In this paper, we introduce the iterative closest ellipsoidal transform (ICET), a novel three-dimensional (3D) lidar scan-matching algorithm that re-envisions the normal distributions transform (NDT) in order to provide robust accuracy prediction from first principles. Like NDT, ICET subdivides a lidar scan into voxels in order to analyze complex scenes by considering many smaller local point distributions; however, ICET assesses the voxel distribution to distinguish random noise from deterministic structure. ICET then uses a weighted least-squares formulation to incorporate this noise/structure distinction while computing a localization solution and predicting the solution-error covariance. To demonstrate the reasonableness of our accuracy predictions, we verify 3D ICET in three lidar tests involving real-world automotive data, high-fidelity simulated trajectories, and simulated corner-case scenes. For each test, ICET consistently performs scan matching with sub-centimeter accuracy. With this level of accuracy, combined with the fact that the algorithm is fully interpretable, this algorithm is well suited for safety-critical transportation applications. Code is available at https://github.com/mcdermatt/ICET.

Kyle A. Lethander, Clark N. Taylor

To understand the error sources present in inertial sensors, both the white (time-invariant) and correlated noise sources must be properly characterized. To understand both sources, the standard approach (IEEE standards 647-2006, 952-2020) is to compute the Allan variance of the noise and then use human-based interpretation of linear trends to estimate the separate noise sources present in a sensor. Recent work has sought to overcome the graphical nature and visual-inspection basis of this approach leading to more accurate noise estimates. However, when using noise characterization in a filter, it is important that the noise estimates be not only accurate but also conservative, i.e., that the estimated noise parameters overbound truth. In this paper, we propose a novel method for automatically estimating conservative noise parameters using the Allan variance. Results of using this method to characterize a low-cost MEMS IMU (Analog Devices ADIS16470) are presented, demonstrating the efficacy of the proposed approach.

Li-Ta Hsu, Feng Huang, Hoi-Fung Ng et al.

Accurate positioning in urban canyons remains a challenging problem. To facilitate the research and development of reliable and precise positioning methods using multiple sensors in urban canyons, we built a multisensory dataset, UrbanNav, collected in diverse, challenging urban scenarios in Hong Kong. The dataset provides multi-sensor data, including data from multi-frequency global navigation satellite system (GNSS) receivers, an inertial measurement unit (IMU), multiple light detection and ranging (lidar) units, and cameras. Meanwhile, the ground truth of the positioning (with centimeter-level accuracy) is postprocessed by commercial software from NovAtel using an integrated GNSS real-time kinematic and fiber optics gyroscope inertial system. In this paper, the sensor systems, spatial and temporal calibration, data formats, and scenario descriptions are presented in detail. Meanwhile, the benchmark performance of several existing positioning methods is provided as a baseline. Based on the evaluations, we conclude that GNSS can provide satisfactory results in a middle-class urban canyon if an appropriate receiver and algorithms are applied. Both visual and lidar odometry are satisfactory in deep urban canyons, whereas tunnels are still a major challenge. Multisensory integration with the aid of an IMU is a promising solution for achieving seamless positioning in cities. The dataset in its entirety can be found on GitHub at https://github.com/IPNL-POLYU/UrbanNavDataset.

Sriramya Bhamidipati, Tara Mina, Grace Gao

There is growing interest in designing a future lunar navigation satellite system (LNSS) while utilizing a SmallSat platform. However, many design decisions, e.g., regarding the satellite clock and lunar orbit, are yet to be finalized. In our prior work, we developed an LNSS architecture that leverages intermittently available Earth-GPS signals to compute timing corrections, thereby alleviating the need for a higher-grade onboard clock. In this work, we formulate twenty case studies with different grades of clocks and lunar orbits to analyze the trade-offs in designing a SmallSat-based LNSS with time transfer from the Earth GPS. For each case study, the accuracy of ranging signals is assessed via the lunar user equivalent range error (UERE). Even with lower-grade clocks, the lunar UERE exhibits performance comparable to that of the Earth GPS. Furthermore, variations in the lunar UERE are also examined when the available Earth-GPS measurements are processed at different rates.

Lorenzo Ortega, Charly Poulliat

In this article, we present the design of a new navigation message system that includes an error-correcting scheme. This design exploits the “carousel” nature of the broadcast navigation message and facilitates (i) a reduction in the time to first fix (TTFF) and (ii) enhanced error-correcting performance under both favorable and challenging channel conditions. We show here that this combination design requires error-correcting schemes characterized by maximum distance separable (MDS) and full diversity properties. Error-correcting Root low density parity check (Root-LDPC) codes operate efficiently to block various channels and thus can permit efficient and rapid recovery of information over potentially non-ergodic channels. Finally, to ensure appropriate data demodulation in harsh environmental conditions, we propose the use of Root-LDPC codes endowed with a nested property which will permit them to adjust the channel coding rate depending on the number of information blocks received. The proposed error-correcting combination design was then simulated and compared with the well-known GPS L1C subframe 2 using several different transmission scenarios. The results of these simulations revealed some enhancement of the error-correcting performance and reductions in TTFF in several specific situations.

Sijie Lyu, Yan Xiang, Tiantian Tang et al.

Ionospheric augmentation is one of the most important dependences of PPP-RTK. Because of the dispersive features of the ionosphere, the ionospheric information is usually coupled with satellite- and receiver-related biases. This will pose a hidden trouble of inconsistent ionospheric corrections if different numbers of reference stations are involved in calculation. In this paper, we aimed at introducing a consistent regional vertical ionospheric model (RVIM) by estimating receiver biases. We first presented the inconsistent ionospheric corrections under sparse networks. Then the RVIM is compared with the International GNSS Service (IGS) final global ionospheric map (GIM) product, and the average of differences between them is 1.13 TECU. Furthermore, the slant ionospheric corrections were employed as a reference to evaluate both RVIM and GIM. The mean RMS values are 1.48 and 2.23 TECU for the RVIM and GIM, respectively. Finally, we applied the RVIM into PPP-RTK. Results indicate that the PPP-RTK with RVIM constraints achieves improvements in horizontal errors, vertical errors, and convergence time by 43.45, 29.3, and 22.6% under the 68% confidence level, compared with the conventional PPP-AR.

Aiden Morrison, Nadezda Sokolova, Nicolai Gerrard et al.

The extent to which navigation signals in the E6 band may be impacted by shared spectrum allocations might be underappreciated. This paper presents top-level observations from a multi-year international radio frequency interference (RFI) monitoring project covering all L-band global navigation satellite system (GNSS) signals with specific focus on the challenges facing the E6 band. The context of this paper is the assumption that most users will be non-authorized and have access to only the open data-bearing signal component and not the encrypted pilot of the E6 Galileo signal. In virtually all locations where the Advanced RFI Detection, Analysis, and Alerting System (ARFIDAAS) monitoring stations were deployed, frequent disruption of the E6 band from systems such as radar installations or other authorized users of the spectrum was observed. In the presented paper, an effort is made to put the observations in the context of the expected use cases of the E6 signal.

Akshay Shetty, Timmy Hussain, Grace Gao

Communication connectivity is desirable for the safe and efficient operation of multi-robot systems. While decentralized algorithms for connectivity maintenance have been explored in recent literature, the majority of these works do not account for robot motion and sensing uncertainties. These uncertainties are inherent in practical robots and result in robots deviating from their desired positions which could potentially result in a loss of connectivity. In this paper, we present a decentralized connectivity maintenance algorithm accounting for robot motion and sensing uncertainties (DCMU). We, first, propose a novel weighted graph definition for the multi-robot system that accounts for the aforementioned uncertainties along with realistic connectivity constraints such as line-of-sight connectivity and collision avoidance. We, then, design a decentralized gradient-based controller for connectivity maintenance with which we derive the gradients of the weighted graph edge weights required for computing the control. Finally, we perform multiple simulations to validate the connectivity maintenance performance of our DCMU algorithm under robot motion and sensing uncertainties, showing an improvement compared to previous work.

Shijie Li, Chengqi Xu, Jialun Liu

Abstract Inland waterway transportation is one of the most important means to transport cargo in rivers and canals. To facilitate autonomous navigation for ships in inland waterways, this paper proposes a data-driven approach for predictions and control of underactuated ships with unknown dynamics, which integrates model predictive control (MPC) with an iterative learning control (ILC) scheme. In each iteration, kernel-based linear regressors are used to identify the relations between the evolution of ship states and control inputs based on the stored data from previous iterations and the collected data during operation, so as to build the system prediction model. The data are dynamically used to fix the prediction model over iterations, as well as to improve the controller performance until it converges. The proposed approach does not require prior knowledge regarding the hydrodynamic coefficients and ship parameters, but learns from the data instead. In addition, it exploits the advantages of MPC in handling constraints with minimised overall cost. Simulation results show that the controller could start from a nominal, linear data-driven ship model and then learn to reduce the path-following errors based on the data obtained over iterations.

Ning Zhou, Lawrence Lau, Ruibin Bai et al.

Ultrawideband (UWB) technology has received considerable attention in indoor positioning because of its high ranging accuracy. However, UWB range measurements can be contaminated by the delayed signals resulting from obstruction and reflection in difficult indoor environments. These signals introduce delays to range measurements and degrade positioning accuracy if they are not resolved properly. In order to mitigate the effects of delayed range measurements on positioning and achieve a high-accuracy position estimation, this paper proposes a robust particle-filter-based indoor positioning algorithm. In the proposed algorithm, an outlier detection method is proposed for delayed measurement identification, and a constrained particle sampling method is proposed to optimize the distribution of the predicted particles. The proposed algorithm is assessed rigorously through testing. The test results show that the proposed algorithm can effectively identify delayed range measurements, mitigate their effects on position estimation, and improve positioning accuracy.

Chen Zhu, Michael Meurer, Christoph Günther

Camera-based visual navigation has great potential for various applications, especially in satellite-signal-degenerated environments. However, the lack of integrity protection has constrained its utilization in safety-critical applications. Integrity characterizes the quality of the information that a navigation system delivers. Integrity frameworks have been developed over decades for satellite navigation, and continue to play an essential role in safety-critical applications like civil aviation. Nevertheless, there are several challenges to quantify the risks associated with visual navigation. Over the last few years, several approaches to tackle these challenges have been investigated. These developments are the first steps toward a reliable visual positioning framework with integrity monitoring capabilities. In this paper, we review the current status, particular challenges, and development trends in visual positioning integrity monitoring. In addition, we propose a preliminary framework so that the future developments on visual navigation integrity can benefit from a systematic approach.

Chen Zhuang, Hongbo Zhao, Yuli He et al.

High-precision positioning methods have drawn great attention in recent years due to the rapid development of smart vehicles as well as automatics driving technology. The Real-Time Kinematic (RTK) technique is a mature tool to achieve centimeter-level positioning accuracy in open-sky areas. However, the users who drive under dense urban conditions are always confronted with harsh global navigation satellite system (GNSS) environments. Skyscrapers and overpasses block the signals and reduce the number of visible satellites, making it difficult to achieve continuous and precise positioning. Considering that the road is relatively smooth in most urban areas, vehicles are expected to travel on the same plane when they are close to each other. The road plane information is a promising candidate to enhance the performance of the RTK method in constrained environments. In this paper, we propose a plane-constrained RTK (PCRTK) method using the positioning information from cooperative vehicles. In a vehicle-to-vehicle (V2V) network, the positions of cooperative vehicles are used to fit a road plane for the target vehicle. The parameters of the plane fitting are treated as new measurements to enhance the performance of the float estimator. The relationship between the plane parameters and the state of the estimator is derived in our study. To validate the performance of the proposed method, several experiments with a four-vehicle fleet were carried out in open-sky areas and dense urban areas in Beijing, China. Simulations and experimental results show that the proposed method can take advantage of the plane constraint and obtain more accurate positioning results compared to the traditional RTK method.