The ATENEA (Advanced Techniques for Navigation Receivers and Applications) project aims to join deeply integrated GNSS/INS receiver architectures and LIDAR techniques to provide an advanced navigation solution. The approach is suitable for a wide range of surveying applications in difficult environments, being Urban Mapping selected as reference case. ATENEA tackles the most challenging issues of this type of applications, showing how the use of Galileo signals, integrated positioning and observable processing can in one shot solve the more severe technical issues (robustness and continuity), increase accuracy and drastically reduce the system cost. The goal of the ATENEA project is to develop an advanced technology concept for seamless navigation at the cm-level regardless of the environment.
PurposeExisting studies are scarce, especially on the Industry 4.0 application to firms' innovation and competitiveness, and even more on the application to LATAM and Spanish SMEs. This paper tries to fill this gap by explaining the results of applying a systematic model, to understand which are the SMEs' strengths and weaknesses in relation to the Industry 4.0 transformation.Design/methodology/approachA systematic methodology involving documentation analysis, visits to the companies, interviews with employees and managers, making a preliminary diagnosis, crossing their needs with the enablers that can apply. The fieldwork was carried out during a two month period (2019), on a target sample of 22 SMEs operating under industrial productive activity already exporting or planning their internationalization toward LATAM regions.FindingsThere are relevant barriers that need to be overcome in order to enter Industry 4.0 and, in this specific analysis, the following major classification was obtained: (1) Technological barrier, (2) Training barrier, (3) Economic barrier and (4) Contextual barrier.Originality/valueThis paper provides new insights and sets a starting point regarding LATAM and Spanish’ Industry 4.0 situation, while contributing to the SMEs competitiveness by providing deeper understanding of the barriers and limitations in adopting Industry 4.0, pointing out some implications and suggestions for organizations to implement.
Air surveillance radar tracking systems present a variety of known problems related to uncertainty and lack of accurately in radar measurements used as source in these systems. In this work, we feature the theoretical aspects of a tracking algorithm based on neural network paradigm where, from discrete measurements provided by surveillance radar, the objective will be to estimate the target state for tracking purposes as accuracy as possible. The absence of an optimal statistical solution makes the featured neural network attractive despite the availability of complex and well-known filtering algorithms. Neural networks exhibit universal mapping capabilities that allow them to be used as a control tool for capturing hidden information about models learned from a dataset. We use these capabilities to let the network learn, not only from the received radar measurement information, but also from the aircraft maneuvering context, contextual information, where tracking application is working, taking into account this new contextual information which could be obtained from predefined, commonly used, and well-known aircraft trajectories. In this case study, the proposed solution is applied to a typical air combat maneuvering, a dogfight, a form of aerial combat between fighter aircraft. Advantages of integrating contextual information in a neural network tracking approach are demonstrated.
In this paper, an Orthogonal Frequency-Division Multiple Access (OFDMA) signal based on the IEEE 802.16 WiMAX standard with integrated navigation and communication capabilities is presented. The work has been carried out by DEIMOS Space under ESA contract for the Feasibility Study for a Reduced Planetary Navigation and Communication System (PLANCOM), which aims the definition of a local infrastructure for future Mars and Moon exploration mission using an integrated, flexibly and low-cost approach. An integrated communications and navigation signal covering all requirements for human, robotic, surface assets, and orbit vehicles in a planetary environment (in the context of this paper, "planetary" refers to Mars and Moon) has been devised, following the guidelines of the Aurora Programme.
This paper studies Bayesian filtering techniques applied to the design of advanced delay tracking loops in GNSS receivers with multipath mitigation capabilities. The analysis includes tradeoff among realistic propagation channel models and the use of a realistic simulation framework. After establishing the mathematical framework for the design and analysis of tracking loops in the context of GNSS receivers, we propose a filtering technique that implements Rao-Blackwellization of linear states and a particle filter for the nonlinear partition and compare it to traditional delay lock loop/phase lock loop-based schemes.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.