Joint Navigation Research Articles

Electronic Warfare Theory and Strategy: Focusing on the Threat of North Korea’s Electronic Warfare

Purpose: The importance of electronic warfare in modern warfare is increasing day by day. In particular, North Korea has a relative weakness of conventional weapons systems, and has focused on developing asymmetric weapons systems and electronic warfare as a way to overcome them. North Korea is currently expected to further expand its application to some aircraft and guided weapons operations under satellite navigation systems such as GPS, GLONASS, COMPASS and Beidou.
 Method: This study studied the prior theory of electronic warfare at home and abroad. Prior research examined the military doctrines of the United States and trends in Europe. In particular, I studied the doctrines of the United States Joint Navigation Center (Navigation Warfare Center, JNWC) and the Education Command. Based on these prior research materials and literature reviews, I wanted to specifically review North Korea's electronic warfare cases and present our response in terms of legal and institutional aspects.
 Results: The South Korean military should recognize the importance of navigation warfare (part of electronic warfare) that protects our troops' location, navigation and square information so that they can be used without interruption, prevent the enemy's use of related information, and ensure the peaceful use of such information outside the light bulb In addition, we need to build an electronic warfare simulator and continue to promote training based on various scenarios.
 Conclusion: Efforts to train and support professionals are urgently needed. In parallel with these efforts, joint efforts to develop electronic technologies between the civilian and military should be preceded. To win a high-tech war in the 21st century, it is essential, among other things, to have precision weapons and original electronic warfare support capabilities. In addition, it is necessary to foster R&D and defense companies to secure domestic technology.

Pulsar and Magnetar Navigation with Fermi/GBM and GECAM

The determination of the absolute and relative position of a spacecraft is critical for its operation, observations, data analysis, scientific studies, as well as deep-space exploration in general. A spacecraft that can determine its own absolute position autonomously may perform better than those that must rely on transmission solutions. In this work, we report an absolute navigation accuracy of ∼20 km using 16 day Crab pulsar data observed with Fermi’s Gamma-ray Burst Monitor (GBM). In addition, we propose a new method with the inverse process of the triangulation for joint navigation using repeated bursts like those from the magnetar SGR J1935+2154 observed by the Gravitational-wave High-energy Electromagnetic Counterpart All-sky Monitor and GBM.

A Distributed Formation Joint Network Navigation and Positioning Algorithm

In view of the problem that the leader-follower joint navigation scheme relies too much on the absolute navigation and positioning accuracy of the leader node, under the conditions of distributed network-centric warfare (NCW) and to meet the location service accuracy, reliability, and synergy efficiency of the future integrated communication, navigation (ICN), we built a joint navigation and positioning system with low Earth orbit (LEO), airborne data link, and inertial navigation system (INS) as the core; designed a ranging and time-synchronization scheme of the joint navigation and positioning system; and established a joint navigation and positioning method for formation and networking based on mutual ranging and velocity measurement information between aircrafts. Finally, based on the designed LEO constellation, the universality, effectiveness, superiority, and potential superiority of algorithm are verified, respectively. The simulation results show that the scheme can meet the requirements of joint location services in challenging environments, and could be used as a reference scheme for future ICN integration.

Vibration Attenuation and Joint Navigation through Robust Control System Hardware for Multi-Degrees-of-Freedom Flexible Robots

Vibration Attenuation and Joint Navigation through Robust Control System Hardware for Multi-Degrees-of-Freedom Flexible Robots

Joint navigation technology based on space and ground measurements for Chang’e-5 near-Earth return trajectory

Joint navigation technology based on space and ground measurements for Chang’e-5 near-Earth return trajectory

Spatially variant autofocus for circular synthetic aperture sonar.

Circular synthetic aperture sonar (CSAS) is a method for improving the resolution and target detection capabilities of a synthetic aperture sonar system. CSAS data are difficult to focus because of their large aperture sizes and elevation sensitivity. This difficulty has sometimes been addressed by using transponders or distributing isotropic scatterers in the field of view of the system; however, this comes at the cost of reduced practicality. As an alternative, map-drift based multipoint autofocus ("multilateration") was proposed by Cantalloube and Nahum [IEEE Trans. Geosci. Remote Sens. 49, 3730-37 (2011)] for autofocusing analogous circular synthetic aperture radar data. Multilateration also solves the problem of aberration spatial variance by providing a three-dimensional navigation correction. In circular synthetic aperture focusing problems, though, correcting aberrations is a joint navigation and elevation estimation problem, and the present work extends the multilateration approach to simultaneously solve both a navigation solution and coordinate corrections for the multilateration control patches. Additionally, methods for addressing the stability and behavior of the inverse problem are addressed, and an adaptive weighting scheme for reducing the influence of outliers is presented. The field results demonstrate near optimal point-spread functions on distributions of natural isotropic scatterers and robustness in regions with bathymetric variability.

Determination of the added-liquid masses in the problems of control of the motion and stealthiness of marine underwater objects

A numerical method for calculating the generalized additional masses of sea underwater objects with fins, performing both single and joint navigation of several objects, as well as with elastic vibrations of the hull, is presented. The flow is modeled using a continuous layer of sources and vortex frames that simulate the body and its fins. The method provides a strict account of the mutual influence of bodies and their elements and does not impose restrictions on their shape. Approbation of the method showed its reliability and the possibility of practical use in design practice, as well as in simulators and on-board complexes.

Dynamic Bayesian Collective Awareness Models for a Network of Ego-Things

A novel approach is proposed for multimodal collective awareness (CA) of multiple networked intelligent agents. Each agent is here considered as an Internet-of-Things (IoT) node equipped with machine learning capabilities; CA aims to provide the network with updated causal knowledge of the state of execution of actions of each node performing a joint task, with particular attention to anomalies that can arise. Data-driven dynamic Bayesian models learned from multisensory data recorded during the normal realization of a joint task (agent network experience) are used for distributed state estimation of agents and detection of abnormalities. A set of switching dynamic Bayesian network (DBN) models collectively learned in a training phase, each related to particular sensorial modality, is used to allow each agent in the network to perform synchronous estimation of possible abnormalities occurring when a new task of the same type is jointly performed. Collective DBN (CDBN) learning is performed by unsupervised clustering of generalized errors (GEs) obtained from a starting generalized model. A growing neural gas (GNG) algorithm is used as a basis to learn the discrete switching variables at the semantic level. Conditional probabilities linking nodes in the CDBN models are estimated using obtained clusters. CDBN models are associated with a Bayesian inference method, namely, distributed Markov jump particle filter (D-MJPF), employed for joint state estimation and abnormality detection. The effects of networking protocols and of communications in the estimation of state and abnormalities are analyzed. Performance is evaluated by using a small network of two autonomous vehicles performing joint navigation tasks in a controlled environment. In the proposed method, first the sharing of observations is considered in ideal condition, and then the effects of a wireless communication channel have been analyzed for the collective abnormality estimation of the agents. Rician wireless channel and the usage of two protocols (i.e., IEEE 802.11p and IEEE 802.15.4) along with different channel conditions are considered as well.

Getting There Together: Group Navigation in Distributed Virtual Environments.

We analyzed the design space of group navigation tasks in distributed virtual environments and present a framework consisting of techniques to form groups, distribute responsibilities, navigate together, and eventually split up again. To improve joint navigation, our work focused on an extension of the Multi-Ray Jumping technique that allows adjusting the spatial formation of two distributed users as part of the target specification process. The results of a quantitative user study showed that these adjustments lead to significant improvements in joint two-user travel, which is evidenced by more efficient travel sequences and lower task loads imposed on the navigator and the passenger. In a qualitative expert review involving all four stages of group navigation, we confirmed the effective and efficient use of our technique in a more realistic use-case scenario and concluded that remote collaboration benefits from fluent transitions between individual and group navigation.

A benchmarking measurement campaign in GNSS-denied/challenged indoor/outdoor and transitional environments

Abstract Localization in GNSS-denied/challenged indoor/outdoor and transitional environments represents a challenging research problem. This paper reports about a sequence of extensive experiments, conducted at The Ohio State University (OSU) as part of the joint effort of the FIG/IAG WG on Multi-sensor Systems. Their overall aim is to assess the feasibility of achieving GNSS-like performance for ubiquitous positioning in terms of autonomous, global, preferably infrastructure-free positioning of portable platforms at affordable cost efficiency. In the data acquisition campaign, multiple sensor platforms, including vehicles, bicyclists and pedestrians were used whereby cooperative positioning (CP) is the major focus to achieve a joint navigation solution. The GPSVan of The Ohio State University was used as the main reference vehicle and for pedestrians, a specially designed helmet was developed. The employed/tested positioning techniques are based on using sensor data from GNSS, Ultra-wide Band (UWB), Wireless Fidelity (Wi-Fi), vison-based positioning with cameras and Light Detection and Ranging (LiDAR) as well as inertial sensors. The experimental and initial results include the preliminary data processing, UWB sensor calibration and Wi-Fi indoor positioning with room-level granularity and platform trajectory determination. The results demonstrate that CP techniques are extremely useful for positioning of platforms navigating in swarms or networks. A significant performance improvement in terms of positioning accuracy and reliability is achieved. Using UWB, decimeter-level positioning accuracy is achievable under typical conditions, such as normal walls, average complexity buildings, etc. Using Wi-Fi fingerprinting, success rates of approximately 97 % were obtained for correctly detecting the room-level location of the user.

Learnings from Local Collaborative Transformations: Setting a Basis for a Sustainability Framework

The complexity of the sustainability challenge demands for collaboration between different actors, be they governments, businesses, or grassroots movements, at all levels. Nevertheless, and according to previous research, many tensions and obstacles to partnership still exist and results are far from meaningful. By investigating potential synergies, our purpose is to define a sustainability framework to promote better collaboration between community-based initiatives and local governments, in the context of transformation. Specifically, the research aim presented in this paper is to harvest learnings from existing collaborative experiments at the municipal level. As a starting point and using exploratory literature review concerning areas like policy (e.g., public administration) or business and management research, we propose a ‘Compass for Collaborative Transformation’. This heuristic device can support the study of these sustainability experiments. We also introduce a method to map the governance imprint of these collaborations and to provide a ‘proxy’ of transformative efforts. We then present and discuss results from 71 surveyed cases happening in 16 countries in America and Europe, comparing distinctive frameworks involved. Finally, we consider the preconditions of a framework to improve these local collaborations—namely the capacity to support joint navigation through transformative efforts, facing high levels of uncertainty and complexity—and present ongoing efforts to codesign a new sustainability framework.

Dynamic Radar Network of UAVs: A Joint Navigation and Tracking Approach

Nowadays there is a growing research interest on the possibility of enriching small flying robots with autonomous sensing and online navigation capabilities. This will enable a large number of applications spanning from remote surveillance to logistics, smarter cities and emergency aid in hazardous environments. In this context, an emerging problem is to track unauthorized small unmanned aerial vehicles (UAVs) hiding behind buildings or concealing in large UAV networks. In contrast with current solutions mainly based on static and on-ground radars, this paper proposes the idea of a dynamic radar network of UAVs for real-time and high-accuracy tracking of malicious targets. To this end, we describe a solution for real-time navigation of UAVs to track a dynamic target using heterogeneously sensed information. Such information is shared by the UAVs with their neighbors via multi-hops, allowing tracking the target by a local Bayesian estimator running at each agent. Since not all the paths are equal in terms of information gathering point-of-view, the UAVs plan their own trajectory by minimizing the posterior covariance matrix of the target state under UAV kinematic and anti-collision constraints. Our results show how a dynamic network of radars attains better localization results compared to a fixed configuration and how the on-board sensor technology impacts the accuracy in tracking a target with different radar cross sections, especially in non line-of-sight (NLOS) situations.

A Wireless Local Positioning System Concept and 6D Localization Approach for Cooperative Robot Swarms Based on Distance and Angle Measurements

In wireless sensor networks, spatially distributed nodes provide location-dependent sensor information. Therefore, knowledge about the 3D position of all nodes is crucial for the numerous applications that require autonomous mobility. Furthermore, to acquire the nodes' poses and the complete 6D network constellation, the 3D orientation of each node is also required. While many theoretical localization concepts exist for wireless sensor networks, there is still a lack of reliable system and localization concepts which enable robust real-time tracking in real-world scenarios. Therefore, we present a system approach based on an advanced 24 GHz wireless local positioning system, providing distance and angle measurements between pairs of nodes. Furthermore, an extended Kalman filter based localization algorithm is proposed, which evaluates these measurements to track the time varying 6D poses of all nodes in the network. Because only relative measurements are available, one node is chosen to define a joint navigation system. Hence, the proposed system works without any previously installed infrastructure or prior information of the network. The system and localization algorithm are validated by measurements performed in a mobile wireless sensor network comprising six nodes in an indoor scenario with strong multipath propagation. However, despite the challenging environment, the system allows for a stable and accurate 6D pose estimation of all robots in the network with 3D positioning root mean square errors of 6 to 15cm.

Joint navigation performance of distant retrograde orbits and cislunar orbits via LiAISON considering dynamic and clock model errors

Joint navigation performance of distant retrograde orbits and cislunar orbits via LiAISON considering dynamic and clock model errors

Geospatial Mapping of Late-Life Couplehood: Dimensions of Joint Participation.

For older adults aging in place, community mobility affects a constellation of interconnected occupations and can become a problematic aspect of the person-place relationship. The purpose of this research was to examine an older couple's mutually negotiated change in their community mobility and navigation patterns. Spatial data (using global positioning system [GPS] trackers), maps, activity logs, interviews, and well-being measures were collected longitudinally for both members of a long-married community-dwelling couple. Analyses showed changing patterns of occupational engagement and declining navigation and function over time, as well as three dimensions of joint navigation (intersection of health and abilities, perpetuation of long-term roles through participation, and an orientation toward future potentialities). Findings suggest that joint community navigation is multidimensional and may buffer individual functional losses as well as maximize perception of and performance in meaningful activities. This study also demonstrates the utility of mapping methods to better understand occupation for community-dwelling populations.

IONavi

The proliferation of mobile computing has prompted navigation to be one of the most attractive and promising applications. Conventional designs of navigation systems mainly focus on either indoor or outdoor navigation. However, people have a strong need for navigation from a large open indoor environment to an outdoor destination in real life. This article presents IONavi, a joint navigation solution, which can enable passengers to easily deploy indoor-outdoor navigation service for subway transportation systems in a crowdsourcing way. Any self-motivated passenger records and shares individual walking traces from a location inside a subway station to an uncertain outdoor destination within a given range, such as one kilometer. IONavi further extracts navigation traces from shared individual traces, each of which is not necessary to be accurate. A subsequent following user achieves indoor-outdoor navigation services by tracking a recommended navigation trace. Extensive experiments are conducted on a subway transportation system. The experimental results indicate that IONavi exhibits outstanding navigation performance from an uncertain location inside a subway station to an outdoor destination. Although IONavi is to enable indoor-outdoor navigation for subway transportation systems, the basic idea can naturally be extended to joint navigation from other open indoor environments to outdoor environments.

A novel joint navigation state error discriminator based on iterative maximum likelihood estimation

To break through the limitations of traditional discriminators used in vector tracking loops, this paper presents an iterative maximum likelihood estimation (IMLE) method for extracting navigation state errors from multi-satellite signals. The IMLE method takes into account both computational cost and estimation accuracy. The associated gradient vector and Hessian matrix of the MLE cost function are derived. The characteristics of the proposed joint discriminator are analyzed based on the properties of the MLE cost function, gradient vector, and Hessian matrix. The effectiveness of IMLE is verified by Monte Carlo simulation.

Адаптация в ГИС архивных карт поволжских губерний съемки А.И. Менде

Abstract: At present many-page archive maps which are not computer-adapted are becoming available from archival storage, libraries and private collections. We are presenting the results of processing five topographic boundary maps of The Volga Region (including Tver, Yaroslavl, Vladimir, Nizhni Novgorod, Simbirsk) out of 8 maps surveyed by A. I. Mende for providing automated access when applying GIS and Internet technologies. The main objectives of the research are creation of raster electronic maps on combining the pages of archive maps, the following formation of maps for geoportals, integration with the data from the other sources. Taking into account a big number of pages in a map, we are proposing a method of virtually combining the pages of a map into one united representation with its factual separation into a system of raster electronic maps for GIS. Virtual combination of the pages is made by representing separate pages into a formed network of pages whose parameters are defined as a result of solving an optimization task according to the data of registration in GIS of the test pages. After that, some other formats of the maps are formed: for the programs of integration of Google Earth, SAS Planeta and Internet realization through Google MAP API. In the Internet access mode there are functions of joint navigation, coordinates reading and coordinates transition: searching on modern and archive populated areas, reading the page URL condition, applying vector data. The formed complexes of raster maps can be used in applied research work (changing the amount of forests, distribution of windmills) and they form a united zone with Russian and foreign resources.

A low power triple‐mode parallel‐processing CMOS receiver for BD‐II/GPS/Galileo joint navigation applications

ABSTRACTThis article presents a low power triple‐mode parallel‐processing CMOS receiver for BD‐II/GPS/Galileo joint navigation applications. An innovative image rejecting parallel‐processing architecture is proposed, which utilizes a single frequency synthesizer, shares the front‐end, and adopts two complex band‐pass filters in two channels separately, aiming for the receiving of BD‐II B1/GPS L1/GALILEO L1 multistandard navigation signals simultaneously. The proposed triple‐mode parallel‐processing receiver is fabricated in a 0.18 μm CMOS process with 22.2 mA power consumption under the triple‐mode parallel‐processing navigation. Measured results show that it has a maximum conversion gain of 101.5 dB and a double‐side‐band noise figure of 3.3 dB with the fixed IF of 7*f0 (f0 = 1.023 MHz), tunable IF bandwidth of 2*f0/4*f0, and more than 34 dB image rejection. © 2013 Wiley Periodicals, Inc. Microwave Opt Technol Lett 55:2616–2620, 2013

A Telepresence Mobile Robot Controlled With a Noninvasive Brain–Computer Interface

This paper reports an electroencephalogram-based brain-actuated telepresence system to provide a user with presence in remote environments through a mobile robot, with access to the Internet. This system relies on a P300-based brain-computer interface (BCI) and a mobile robot with autonomous navigation and camera orientation capabilities. The shared-control strategy is built by the BCI decoding of task-related orders (selection of visible target destinations or exploration areas), which can be autonomously executed by the robot. The system was evaluated using five healthy participants in two consecutive steps: 1) screening and training of participants and 2) preestablished navigation and visual exploration telepresence tasks. On the basis of the results, the following evaluation studies are reported: 1) technical evaluation of the device and its main functionalities and 2) the users' behavior study. The overall result was that all participants were able to complete the designed tasks, reporting no failures, which shows the robustness of the system and its feasibility to solve tasks in real settings where joint navigation and visual exploration were needed. Furthermore, the participants showed great adaptation to the telepresence system.