Navigation Information Research Articles

Performance Assessment of the Medium Frequency R-Mode Baltic Testbed at Sea near Rostock

Global Navigation Satellite Systems (GNSS) are the primary source of position, navigation and timing (PNT) information in the maritime domain. Nevertheless, there is a pressing need for alternative absolute position information to serve as a backup when GNSS is not trustworthy or usable. One possible alternative navigation system is the terrestrial Ranging Mode, also known as the R-Mode. It reuses medium frequency (MF) radio beacons and base stations of the very high frequency data exchange system (VDES) for the transmission of synchronised signals in the service areas of those stations. A large-scale R-Mode testbed is available in the southern Baltic Sea with eight MF radio beacons transmitting R-Mode signals. These signals suffer the self-interference generated by the reflection of the radio wave from the ionosphere at night. The reflected signal, known as the sky wave in the literature, is a form of multipath, which decreases the accuracy of the system. In our work, we present the analysis of measurements obtained with the research vessel Deneb. The horizontal accuracy achieved in the optimal condition was 15.1 m (95%), whereas under the sky wave, the accuracy decreased to 55.3 m. The results are a starting point for the further development of the MF R-Mode system.

From digital divide to digital capital: the role of education and digital skills in social media participation

Social media platforms have become significant media for participating in society. This, and society’s digitalization overall, has resulted in concerns regarding access and inclusion. By combining theories of social media participation and digital inequality, we explore issues regarding the prerequisites of participating through social media platforms, focusing especially on education. Through an analysis of data from a representative survey study in Finland (N = 3724), we illuminate the ambiguity of the perceived obstacles to both digital skills and political participation. We further build on the concept of digital capital to show the significant mediating effect of digital skills on education and participation. By utilizing the ISS (Internet Skills Scale), we break down digital skills into operational, information navigation, social, creative and mobile skills, and show how the operational skills have most significant mediator between education and political participation in social media. In studying digital inequality, we claim that the concept of digital capital is a valuable tool to illuminate the mechanisms for overcoming digital divides through the transaction of other forms of capital into digital capital, and digital capital to other forms of capital, in this case political capital.

Common Android Smartphones and Apps for Cost-Efficient GNSS Data Collection: An Overview

Global Navigation Satellite System (GNSS) sensors are increasingly considered as major sources of precise Position, Navigation, and Timing (PNT) information. Android-based smartphones contain inbuilt GNSS sensors, and they now contribute to a major global GNSS market segment. Various hardware and Android-based applications (App) developments are witnessed to best exploit the current multi-constellation, multi-frequency GNSS environment. Since Android version 7.0 (Nougat) and Location Application Programming Interface (API) 24, recording GNSS raw data in the smartphone became possible. This paper describes the potential of commercial Smartphones and reviews the capabilities of Android Apps for cost-efficient GNSS measurements for the geospatial community and a novel classification of the Apps based on their capabilities and usability. Results on the positioning capability of common smartphones using real-time data show the achievable modest solution precision, and these are suitable for use of the devices in geomatics applications, improved App development, and for IoT-based system and infrastructure development.

A novel robust progressive cubature Kalman filter with variable step size for inertial navigationsystem/global navigation satellite system tightly coupled navigation

SummaryThis article investigates the state estimation problem of the nonlinear system with the large process prior uncertainty but high‐accuracy measurement information, the situation is prone to occur in the inertial navigation system (INS)/global navigation satellite system (GNSS) tightly coupled navigation system. Furthermore, the unknown heavy‐tailed measurement noises induced by the inaccurate prior navigation information and random measurement outliers are also considered. Given existing methods such as progressive cubature Kalman filter (PCKF) cannot effectively solve the above issues, a novel robust PCKF with variable step size (RVSS‐PCKF) is proposed. First, a new Gaussian‐uniform‐mixing inverse Gamma (GUMIG) distribution is presented to model the variable step size and measurement noise. Next, the GUMIG distribution is expressed as a hierarchical Gaussian presentation and then RVSS‐PCKF is derived with the help of the variational Bayesian (VB) inference. In the filter, the state, variable step size and noise statistic are jointly estimated by the fixed‐point iterations. Finally, the simulations and real data of the tightly coupled navigation illustrate the superiority of the filter in terms of accuracy and steady‐state performance.

Attitude Determination for Unmanned Cooperative Navigation Swarm Based on Multivectors in Covisibility Graph

To reduce costs, an unmanned swarm usually consists of nodes with high-accuracy navigation sensors (HAN) and nodes with low-accuracy navigation sensors (LAN). Transmitting and fusing the navigation information obtained by HANs enables LANs to improve their positioning accuracy, which in general is called cooperative navigation (CN). In this method, the accuracy of relative observation between platforms in the swarm have dramatic effects on the positioning results. In the popular research, constructing constraints in three-dimensional (3D) frame could only optimize the position and velocity of LANs but neglected the attitude estimation so LANs cannot maintain a high attitude accuracy when utilizing navigation information obtained by sensors installed during maneuvers over long periods. Considering the performance of the inertial measurement unit (IMU) and other common sensors, this paper advances a new method to estimate the attitude of LANs in a swarm. Because the small unmanned nodes are strictly limited by relevant practical engineering problems such as size, weight and power, the method proposed could compensate for the attitude error caused by strapdown gyroscopic drift, which only use visual vectors built by the targets detected by cameras with the function of range finding. In our method, the coordinates of targets are mainly given by the You Only Look Once (YOLO) algorithm, then the visual vectors are built by connecting the targets in the covisibility graph of the nodes in the swarm. The attitude transformation matrices between each camera frame are calculated using the multivector attitude determination algorithm. Finally, we design an information filter (IF) to determine the attitude of LANs based on the observation of HANs. Considering the problem of positioning reference, the field test was conducted in the open air and we chose to use two-wheeled robots and one UAV to carry out the experiment. The results show that the relative attitude error between nodes is less than 4 degrees using the visual vector. After filtering, the attitude divergence of LANs’ installed low precision IMU can be effectively constrained, and the high-precision attitude estimation in an unmanned CN swarm can be realized.

Conception of an itinerary planning assistant for EV drivers

Electric vehicles (EVs) require charge stops during long trips. Appropriate digital services can ease the usage of EVs and thus could accelerate their adoption. We propose a planning system that interacts with the user's calendar for deriving upcoming journeys. The core of the system is a newly developed itinerary planning algorithm that integrates charge stop planning. The algorithm uses dynamic information distributed between different stakeholders, namely information regarding charging infrastructure and navigational information. A simulation of distinct EV scenarios validates the efficacy of the proposed work.

Multilayer Model-Aided INS Based on Real-Time Corrected Vehicle Dynamics Model

Vehicle dynamics model-aided inertial navigation system (INS) is an effective approach for autonomous navigation when the Global Navigation Satellite System fails, However, the accuracy of the vehicle dynamics model (VDM) will decrease, or even diverge, in low-speed and high-dynamic situations, which leads to the performance degradation of the whole system. The traditional VDM-aided INS uses the velocity information from VDM to estimate and correct INS errors, but most previous research ignored that the acceleration and angular velocity from INS are more accurate than VDM. To reach the full potential of INS and VDM, this paper proposed a three-layer structure for the VDM-aided INS. In the proposed method, the navigation information is divided into the sensor layer, system layer I, and system layer II. In the sensor layer, VDM is corrected by INS through the strong tracking cubature Kalman filter, and the chi-square test is adopted to avoid redundant computation. In the system layer I, INS is aided by the corrected velocity information from VDM. In the system layer II, the position is updated with the velocity and attitude that are corrected in the system layer I. As the result, VDM and INS can be corrected in different layers simultaneously, and the accuracy and robustness of the whole system can be improved. The simulation experiment proved that, with the sensor layer filter, the velocity errors of VDM can be reduced by 35% in low-speed and high-dynamic situations, and reduced by 20% in the normal situation. Then, a 31-minute field test shows that the maximum horizontal position error of the proposed method is 241.8 m, while those of two traditional VDM-aided INS methods are more than 500 m.

CrowdSPaFE: A Crowd-Sourced Multimodal Recommendation System for Urban Route Safety

In the age of information technology, location-based services such as Google Maps, Bing Maps, and Apple Maps have become popular for navigation and traffic information. These services usually consider the shortest path, traffic information, nearby places, and multi-modal alternative route suggestions based on a user’s constraints. Nevertheless, these services do not always provide the best choice in terms of "user safety". Recently, some research and mobile applications have considered safety issues. Notably, none of these are capable of adapting to dynamic, conflicting safety features and real-time user feedback. Recently, a population-based algorithm called "SPaFE" has been introduced, which deals with crowd-sourced data along with historic data. This population-based approach, however, does not give more weight to recent feedback than to earlier feedback and does not consider updated crime reports. Furthermore, this approach does not consider distance and performs poorly in the area with insignificant or zero feedback. Considering the above background, we have introduced the population based algorithm "CrowdSPaFE" to adapt with dynamic crime reports, latest feedback, navigation in areas with insignificant feedback, and a trade-off between distance and safety factors. Finally, our empirical results of the "CrowdSPaFE" algorithm depict that it significantly outperforms the state of the art.

Cybersecurity risk assessment of VDR

AbstractThe voyage data recorder (VDR) is a data recording system that aims to provide all navigational, positional, communicational, sensor, control and command information for data-driven investigation of accidents onboard ships. Due to the increasing dependence on interconnected networks, cybersecurity threats are one of the most severe issues and critical problems when it comes to safeguarding sensitive information and assets. Cybersecurity issues are extremely important for the VDR, considering that modern VDRs may have internet connections for data transfer, network links to the ship's critical systems and the capacity to record potentially sensitive data. Thus, this research adopted failure modes and effects analysis (FMEA) to perform a cybersecurity risk assessment of a VDR in order to identify cyber vulnerabilities and specific cyberattacks that might be launched against the VDR. The findings of the study indicate certain cyberattacks (false information, command injection, viruses) as well as specific VDR components (data acquisition unit (DAU), remote access, playback software) that required special attention. Accordingly, preventative and control measures to improve VDR cybersecurity have been discussed in detail. This research makes a contribution significantly to the improvement of ship safety management systems, particularly in terms of cybersecurity.

Research on the reform of marine education in the era of intelligent ship development

Intelligent ships integrate new technologies such as information technology and artificial intelligence, and have significant characteristics such as safety and reliability, energy conservation and environmental protection, and economic efficiency. It is the key direction of the future development of commercial ships. Higher education in navigation under the background of ship intelligence should make full use of the advantages of traditional navigation majors, actively master the core technology of intelligent ship development, and actively carry out the cross-integration of computer science, control theory, communication science, artificial intelligent and other information sciences. High-quality compound "new engineering" navigation talents who master information technology and navigation science are cultivated under the background of intelligent ships. Under the background of intelligent ships, reconstructing the training of professional marine technology professionals in higher vocational colleges is conducive to the transformation and upgrading of traditional navigation majors in higher vocational colleges, and provides cutting-edge exploration for the development of high-quality compound "new engineering" navigation majors.

Bluetooth Low Energy Urban Networks for Ultra-Accurate Geo-Location Applications

BLEUN (Bluetooth Low Energy Urban Networks) was an EU-funded research project exploring applications of BLE for smart mobility. These applications include accurate positioning services, vehicle tracking, IoT proximity applications for public transport, etc. During the 12-month project, the team developed outdoor location services, assets tracking, proximity marketing, POI's information and indoor navigation. BLEUN spawned the BEEHIVE technology (www.thebeehive.world), expanding the BLEUN approach to include also other signals like WiFi and LoRa for maximum adaptability and increasing accuracy to less than 60 centimetres, while sticking to a low-cost approach. This mix of technologies is pooled into a big data platform, where all information is anonymously cross-matched. The project has been recognised with the Certificate of Excellence from the I4MS-SAE Quality Label.

Managing 3D GIS Data for Indoor Environment Using Property Graph Database

With the diversification of three-dimensional (3D) urban space data sources and data collection methods, the Open Geospatial Consortium published 3D spatial data standards in Geography Markup Language (GML). Among them, IndoorGML is a data model for expressing the topological relationship between indoor spaces with advantages in terms of semantic information storage and navigation applications. Conversion into a suitable binary file must be accomplished to use the IndoorGML documents through integration with other data. This study proposes a method of generating graph database objects from the documents of IndoorGML to promote usability and interoperability. The labeled property graph model is designed to reflect IndoorGML’s multilayered graph concept. Moreover, in the proposed graph model, the geometry of physical indoor spaces is segregated into separate surfaces. All elements from IndoorGML are managed with specific labels in Neo4j, the most dominant graph database. By applying the proposed method, the graph database is constructed using the officially distributed IndoorGML documents of three buildings. The number of generated graph objects is determined to confirm whether the IndoorGML features are accurately converted into graph database objects. Furthermore, the utility of the constructed graph database is verified through two scenario-based routing tests consisting of finding the optimal room with the lowest route cost and allocating indoor spaces for patrolling to outdoor security offices through indoor and outdoor data integration. In conclusion, this study confirms that the utility of IndoorGML can be promoted using a graph database system in data integration, data queries, and pathfinding applications while preserving data topology and interoperability.

Investigation of the genetic algorithm in the correction scheme of inertial navigation systems in the conditions of the disappearance of signals from external sensors

A scheme for correcting inertial navigation systems in the output signal at the signals disappearance from external sensors of navigation information is considered. The correction is carried out by using predictive error models of the inertial navigation system built by using a genetic algorithm. Measurement accesses of genetic algorithms used for short-term, medium-term and long-term error prediction of inertial navigation systems are studied. Factors that influence the accuracy of predictive models are identified. Recommendations for the practical use of genetic algorithms in the correction scheme of inertial navigation systems are formulated. Keywords inertial navigation system, absence of external signals, correction, genetic algorithm, measuring access, short-term forecast, long-term forecast, recommendations

Navigation complex of impact unmanned aerial vehicle

The navigation complex of a promising impact unmanned vehicle is studied. With respect to the features of navigation systems are determined the composition of the navigation complex. An adaptive estimation algorithm is chosen to correct the inertial navigation system. The structure of the navigation complex, which provides the best choice of the working contour and is distinguished by ease of implementation on board an unmanned aerial vehicle is developed. The choice of a navigation information sensor external to the inertial system based on the analysis of covariance matrices of estimation errors is carried out. Keywords impact unmanned aerial vehicle, navigation system, estimation algorithm, inertial navigation system, Kalman filter, estimation error covariance matrix

Joint Multi-Domain Resource Allocation and Trajectory Optimization in UAV-Assisted Maritime IoT Networks

The integration of Maritime Internet of Things (M-IoT) technology and unmanned aerial/surface vehicles (UAVs/USVs) has been emerging as a promising navigational information technique in intelligent ocean systems. In this article, we consider the UAV-assisted M-IoT network where USVs offload computation-intensive maritime tasks via non-orthogonal multiple access (NOMA) to the UAV equipped with the mobile-edge computing (MEC) server subject to the UAV mobility. To improve the energy efficiency of offloading transmission and workload computation, we focus on minimizing the total energy consumption by jointly optimizing the USVs’ offloaded workload, transmit power, computation resource allocation, as well as the UAV trajectory subject to the USVs’ latency requirements. Despite the nature of mixed discrete and non-convex programming of the formulated problem, we exploit the vertical decomposition and propose a two-layered algorithm for solving it efficiently. Specifically, the top-layered algorithm is proposed to solve the problem of optimizing the UAV trajectory based on the idea of deep reinforcement learning (DRL), and the underlying algorithm is proposed to optimize the underlying multidomain resource allocation problem based on the idea of the Lagrangian multiplier method. Numerical results are provided to validate the effectiveness of our proposed algorithms as well as the performance advantage of NOMA-enabled computation offloading in terms of overall energy consumption.

浅析民航航行情报对安全飞行的意义

浅析民航航行情报对安全飞行的意义

Tightly Coupled Information Fusion for SINS/DVL/USBL Integrated Navigation of UUV

In order to improve the positioning accuracy and reliability of the navigation system in complex underwater environment, a strapdown inertial navigation system (SINS)/doppler velocity log (DVL)/Ultra-short baseline (USBL) tightly-coupled algorithm is proposed based on a centralized Kalman filter. A new 26-dimensional tightly-coupled measurement model is constructed. Different from the traditional loosely-coupled algorithm, the proposed model directly integrates the original measurement information of each sensor, instead of the navigation information such as position and velocity. Simulation and experiment results show that the proposed algorithm can make the best use of the only original information when the sensor cannot output the correct position and velocity in case of poor measurement conditions. Compared with the loosely-coupled algorithm, the proposed algorithm has better positioning accuracy and higher reliability.

경사 발사 유도무기체계에서 함상 저장 전달정렬 가능성 연구

Shipboard transfer alignment is a procedure for determining the initial navigation information of the ship-mounted guided weapon system’s SINS by receiving the surface ship INS information. In order to improve the navigation accuracy of the ship-launched guided missile system, it is necessary to enhance the precision and accuracy of the shipboard transfer alignment. Generally, the shipboard transfer alignment uses both velocity matching and attitude matching to increase observability. Estimating the misalignment error between SINS and MINS using an extended Kalman filter is necessary to obtain the initial attitude of SINS in shipboard transfer alignment to which attitude matching is applied. Because it takes several minutes for the estimated misalignment error to converge, it is essential to reduce the alignment time, especially in the case of weapon systems. This paper presents the possibility of utilizing the shipboard stored transfer alignment for navigation-grade INS mounted on the inclined launching system. There have been no reports of previous studies on the shipboard stored transfer alignment. In a similar research case, a rapid initial alignment method using the pre-performed alignment results was proposed to reduce the alignment time of tactical-grade INS, which was mounted on the launch platform with MINS. To verify the possibility of the shipboard stored transfer alignment, the existing shipboard transfer alignment was repeatedly performed using an inclined guided missile system deployed in the military. The results show that the repeatability and accuracy of the misalignment error satisfy the initial attitude error requirement. Therefore, based on the test results, it is verified that the shipboard stored transfer alignment for the inclined launching guided missile equipped with a navigation-grade INS is possible.

Application of shaken lattice interferometry based sensors to space navigation

High–sensitivity shaken lattice interferometry (SLI) based sensors have the potential to provide deep space missions with the ability to precisely measure non–gravitational perturbing forces. This work considers the simulation of the OSIRIS-REx mission navigation in the vicinity of Bennu with the addition of measurements from onboard SLI–based accelerometers. The simulation is performed in the Jet Propulsion Laboratory’s (JPL) Mission Analysis, Operations and Navigation Toolkit (MONTE) and incorporates OSIRIS-REx reconstructed trajectory and attitude data from the Navigation and Ancillary Information Facility (NAIF) database. The use of the reconstructed data from NAIF provides realistic true dynamical errors and JPL’s MONTE software allows for a high–fidelity simulation of an integrated trajectory for the filter. The navigation performance and reduction of tracking and complex modelling enabled by the onboard SLI–based sensor are presented for two orbital phases of the OSIRIS–REx mission. Overall, the results show that the addition of SLI–based accelerometer measurements improves navigation performance, when compared to a radiometric tracking only configuration. In addition, results demonstrate that highly–precise accelerometer measurements can effectively replace at least one day of DSN passes over a three–day period, thereby reducing tracking requirements. Furthermore, it is shown that lower–fidelity surface force modeling and parameter estimation is required when using onboard SLI–based accelerometers.

A second-order dynamic and static ship path planning model based on reinforcement learning and heuristic search algorithms

Ship path planning plays an important role in the intelligent decision-making system which can provide important navigation information for ship and coordinate with other ships via wireless networks. However, existing methods still suffer from slow path planning and low security problems. In this paper, we propose a second-order ship path planning model, which consists of two main steps, i.e., first-order static global path planning and second-order dynamic local path planning. Specifically, we first create a raster map using ArcGIS. Second, the global path planning is performed on the raster map based on the Dyna-Sarsa(lambda) model, which integrates the eligibility trace and the Dyna framework on the Sarsa algorithm. Particularly, the eligibility trace has a short-term memory for the trajectory, which can improve the convergence speed of the model. Meanwhile, the Dyna framework obtains simulation experience through simulation training, which can further improve the convergence speed of the model. Then, the improved ship trajectory prediction model based on stacked bidirectional gated recurrent unit is used to identify the risk of ship collision and switch the path planning from the first order to the second order. Finally, the second-order dynamic local path planning is presented based on the FCC-A* algorithm, where the cost function of the traditional path planning A* algorithm is rewritten using the fuzzy collision cost membership function (fuzzy collision cost, FCC) to reduce the collision risk of ships. The proposed model is evaluated on the Baltic Sea geographic information and ship trajectory datasets. The experimental results show that the eligibility trace and the Dyna learning framework in the proposed model can effectively improve the planning efficiency of the ship’s global path planning, and the collision risk membership function can effectively reduce the number of collisions in A* local path planning and thus improve the navigation safety of encountering ships.