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  • 1.
    Caizzone, Stefano
    et al.
    German Aerosp Ctr DLR, Inst Commun & Nav, Wessling, Germany.
    Elmarissi, W.
    German Aerosp Ctr DLR, Inst Commun & Nav, Wessling, Germany.
    Marinho, Marco
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Antreich, F.
    Fed Univ Ceara UFC, Dept Teleinformat Engn, Fortaleza, Ceara, Brazil.
    Direction of arrival estimation performance for compact antenna arrays with adjustable size2017In: 2017 IEEE MTT-S INTERNATIONAL MICROWAVE SYMPOSIUM (IMS), Piscataway: IEEE, 2017, p. 666-669, article id 8058657Conference paper (Refereed)
    Abstract [en]

    The quest for compact antenna arrays able to perform robust beamforming and high resolution direction of arrival (DOA) estimation is pushing the antenna array dimensions to progressively shrink, with effects in terms of reduced performance not only for the antenna but also for beamforming and DOA estimation algorithms, for which their assumptions about the antenna properties do not hold anymore. This work shows the design and development of an antenna array with adjustable mutual distance between the single elements: such setup will allow to scientifically analyse the effects that progressive miniaturization, i.e. progressively smaller mutual distances between the antennas, have on the DOA estimation algorithms, as well as show the improvements obtained by using array interpolation methods, i.e. techniques able to create a virtual array response out of the actual array one, such as to comply with the algorithms’ requirements on the antenna response. © 2017 IEEE.

  • 2.
    Marinho, Marco A. M.
    et al.
    University of Brasilia, Brasilia, Brazil.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS), Centre for Research on Embedded Systems (CERES).
    Antreich, Felix
    Federal University of Ceara, Fortaleza, Brazil.
    da Costa, João Paulo Carvalho Lustosa
    University of Brasilia, Brasilia, Brazil.
    Pignaton de Freitas, Edison
    Federal University of Rio Grande do Sul, Porto Alegre, Brazil.
    Antenna Array Based Localization Scheme for Vehicular Networks2017In: 2017 IEEE International Conference on Computer and Information Technology (CIT) / [ed] Lisa O’Conner, Los Alamitos: IEEE Computer Society, 2017, p. 142-146Conference paper (Refereed)
    Abstract [en]

    Vehicular ad hoc networks (VANETs) are emerging as the possible solution for multiple concerns in road traffic such as mobility and safety. One of the main concerns present in VANETs is the localization and tracking of vehicles. This work presents a passive vehicle localization and tracking method based on direction of arrival (DOA) estimation. The proposed method does not rely on external sources of information such as Global Navigation Satellite Systems (GNSS) and can be used to mitigate the possibility of spoofing or to provide a second independent source of position estimation for integrity purposes. The proposed algorithm uses array signal processing techniques to estimate not only the position but also the direction of other vehicles in network. Furthermore, it is a fully passive method and can alleviate the network load since it does not require any location based data exchange and can be performed by any listening vehicle using the signal of any data transmission. A set of numerical simulations is used to validate the proposed method and the results are shown to be more precise than the average accuracy of Global Position System (GPS) receivers. © Copyright 2017 IEEE

  • 3.
    Marinho, Marco
    et al.
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS). University of Brasília (UnB), Department of Electrical Engineering (ENE), Brasília, Brazil.
    Antreich, Felix
    Department of Teleinformatics Engineering Federal University of Ceará (UFC), Fortaleza, Brazil.
    da Costa, João Paulo C.L.
    University of Brasília (UnB), Department of Electrical Engineering (ENE), Brasília, Brazil.
    Caizzone, Steffano
    German Aerospace Center (DLR), Institute for Communications and Navigation, Wessling, Germany.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Pignaton de Freitas, Edison
    Federal University of Rio Grande do Sul (UFRGS), Informatics Institute, Porto Alegre, Brazil.
    Robust Nonlinear Array Interpolation for Direction of Arrival Estimation of Highly Correlated Signals2018In: Signal Processing, ISSN 0165-1684, E-ISSN 1872-7557, Vol. 144, p. 19-28Article in journal (Refereed)
    Abstract [en]

    Important signal processing techniques need that the response of the different elements of a sensor array has specific characteristics. For physical systems this often is not achievable as the array elements’ responses are affected by mutual coupling or other effects. In such cases, it is necessary to apply array interpolation to allow the application of ESPRIT, Forward Backward Averaging (FBA), and Spatial Smoothing (SPS). Array interpolation provides a model or transformation between the true and a desired array response. If the true response of the array becomes more distorted with respect to the desired one or the considered region of the field of view of the array increases, nonlinear approaches becomes necessary. This work presents two novel methods for sector discretization. An Unscented Transform (UT) based method and a principal component analysis (PCA) based method are discussed. Additionally, two novel nonlinear interpolation methods are developed based on the nonlinear regression schemes Multivariate Adaptive Regression Splines (MARS) and Generalized Regression Neural Networks (GRNNs). These schemes are extended and applied to the array interpolation problem. The performance of the proposed methods is examined using simulated and measured array responses of a physical system used for research on mutual coupling in antenna arrays. © 2017 The Author(s). Published by Elsevier B.V.

  • 4.
    Marinho, Marco
    et al.
    Department of Electrical Engineering (ENE), University of Brasília (UnB), Brazil & Institute for Communications and Navigation, German Aerospace Center (DLR), Germany.
    da Costa, Joao Paulo C. L.
    Department of Electrical Engineering (ENE), University of Brasília (UnB), Brazil & Institute for Communications and Navigation, German Aerospace Center (DLR), Germany & Institute for Information Technology, Ilmenau University of Technology, Ilmenau, Germany.
    Antreich, Felix
    Institute for Communications and Navigation, German Aerospace Center (DLR), Germany.
    Pignaton de Freitas, Edison
    Department of Electrical Engineering (ENE), University of Brasília (UnB), Brazil & Informatics Institute, Federal University of Rio Grande Do sul (UFRGS), Brazil.
    Vinel, Alexey
    Halmstad University, School of Information Technology, Halmstad Embedded and Intelligent Systems Research (EIS).
    Adaptive Communication and Cooperative MIMO Cluster Formation for Improved Lifetime in Wireless Sensor Networks2016In: Proceedings - WiSEE 2016: 2016 IEEE International Conference on Wireless for Space and Extreme Environments, New York: IEEE, 2016, p. 190-195, article id 7877330Conference paper (Refereed)
    Abstract [en]

    One of the main limitations that still keeps Wireless Sensor Networks (WSNs) from being adopted in a large scale is the limited energy supply, i.e. the lifetime of the nodes that constitute the network. The wireless communication between nodes is responsible for most of the energy consumed in WSNs. A promising method to improve the energy efficiency is the usage of a Cooperative Multiple Input Multiple Output (CO-MIMO) scheme, where nodes form clusters to transmit and receive signals using a virtual antenna array. This work presents a study on the energy consumption of multi-hop and single-hop transmission compared to CO-MIMO and how to select the most efficient method. It also proposes a method for adaptively choosing the number of nodes that form a CO-MIMO cluster in order to maximize the lifetime of the network and to avoid disconnections. The proposed method takes into account not only the total energy consumption but also the distribution of energy within the network, aiming to keep the energy distribution across the network as uniform as possible. The effects of the proposed methods in the total available energy of the network and in the distribution of the energy is presented by means of numerical simulations. © 2016 IEEE.

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