Journal Cover
IEEE Aerospace and Electronic Systems Magazine
Journal Prestige (SJR): 0.335
Citation Impact (citeScore): 1
Number of Followers: 259  
 
  Full-text available via subscription Subscription journal
ISSN (Print) 0885-8985
Published by IEEE Homepage  [228 journals]
  • Cover 2
    • Abstract: Presents a listing of the editorial board, board of governors, current staff, committee members, and/or society editors for this issue of the publication.
      PubDate: Dec. 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • AESS MEETINGS & CONFERENCES
    • Abstract: Presents information on AESS society conferences and meetings.
      PubDate: Dec. 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • Cover 4
    • Abstract: Presents the back cover for this issue of the publication.
      PubDate: Dec. 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • In This Issue - Technically
    • Pages: 2 - 3
      PubDate: Dec. 1 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • Cognitive Radar Special Issue—Part 1
    • Authors: Stefan Brüggenwirth;Albert Huizing;Alexander Charlish;
      Pages: 4 - 5
      Abstract: Since the pioneering works by Simon Haykin and Joe Guerci over a decade ago, the field of cognitive radar has become well established in research and industry, as can be clearly seen by the growing number of special sessions, citations, and patents. The objective of this special issue is to provide the readers with both overview articles on the state of the art and philosophy behind cognitive radar, as well as implementation details and application examples. Due to the high number of excellent contributions, we were able to fill two special issues. This first part covers high-level concepts and cognitive radar architectures as well as first experimental achievements. The second part illustrates concrete applications and case studies of cognitive radar systems in practice.
      PubDate: Dec. 1 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • An Overview of Cognitive Radar: Past, Present, and Future
    • Authors: Sevgi Zubeyde Gurbuz;Hugh D. Griffiths;Alexander Charlish;Muralidhar Rangaswamy;Maria Sabrina Greco;Kristine Bell;
      Pages: 6 - 18
      Abstract: Modern radar systems face numerous challenges due to requirements of robust, high performance across multiple missions and multiple functions in the face of dynamically changing environments. Cognitive radar has emerged over the past 15 years through synergy of cybernetics, waveform diversity, and knowledge-aided signal processing as a new vision for the future of radar systems that can address these challenges. This article provides an overview of the historical context and mechanisms for cognitive processing in engineering systems, which have driven an evolution in the definition, analysis, and design of cognitive radar. A survey of cognitive radar research trends is given to provide insight on applications and techniques, while technical and practical challenges to future progress are discussed.
      PubDate: Dec. 1 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • AESS Senior Members Elevated in 2019
    • Pages: 19 - 19
      Abstract: Presents a listing of AESS members who were elevated to the status of Senior Member.
      PubDate: Dec. 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • Cognitive Radar Principles for Defence and Security Applications
    • Authors: Michael Bockmair;Christoph Fischer;Magdalena Letsche-Nuesseler;Christoph Neumann;Michael Schikorr;Markus Steck;
      Pages: 20 - 29
      Abstract: Modern defense and security radar sensors have to operate in a very diverse set of dynamically varying scenarios: Detection, tracking, and classification of very small and slow air targets, such as drones in clutter areas up to fast air targets such as missiles and fighter aircraft together with an increasingly congested and contested spectrum are essential and challenging system requirements. Cognitive radars that combine a multitude of well known and new techniques offer a promising solution to these challenges. In this article, the operational scenarios are introduced, and then a high-level functional architecture is presented, before discussing certain aspects of cognitive radars from a user's and manufacturer's point of view.
      PubDate: Dec. 1 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • Cognitive Radar for Classification
    • Authors: Stefan Brüggenwirth;Marcel Warnke;Simon Wagner;Kilian Barth;
      Pages: 30 - 38
      Abstract: The article presents the cognitive radar architecture of Fraunhofer FHR based on a three-layer model of human cognitive performance. The approach is illustrated using examples for non-cooperative target identification and classification. On the skill based layer, a target-matched waveform design is presented and experimental results are shown. For the transition to the rule-based layer, convolutional neural networks are explained for the identification of air-targets and a novel auto-encoder for change detection is introduced. For rule-based behavior, a policy based decision making algorithm for NCTI waveform selection is explained, using CPOMDPs.
      PubDate: Dec. 1 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • 2019 Aerospace & Electronics Systems Society Distinguished Lecturers
    • Pages: 39 - 39
      Abstract: Presents information on the AESS 2019 Distinguished Lecturers series.
      PubDate: Dec. 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • A Cognitive Radar Testbed for Outdoor Experiments
    • Authors: Roland Oechslin;Sebastian Wieland;Sebastian Hinrichsen;Uwe Aulenbacher;Peter Wellig;
      Pages: 40 - 48
      Abstract: We present a cognitive radar testbed with an adaptive X-band radar sensor and a controller with a perception-action cycle to optimize waveform and processing parameter in real-time. The sensor hardware is described and the processing and optimization algorithms are explained. In the second part of this article, we present results from the outdoor experiments in various environments and with several optimization goals, such as tracking accuracy optimization, bandwidth usage minimization, time effort minimization, and jamming mitigation.
      PubDate: Dec. 1 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • IEEE AEROSPACE & ELECTRONIC SYSTEMS SOCIETY ORGANIZATION 2019
    • Pages: 49 - 49
      Abstract: Reports on the AESS society organization, committees, and board members.
      PubDate: Dec. 2019
      Issue No: Vol. 34, No. 12 (2019)
       
  • Development and Calibration of a Low-Cost Radar Testbed Based on the
           Universal Software Radio Peripheral
    • Authors: Jonas Myhre Christiansen;Graeme E. Smith;
      Pages: 50 - 60
      Abstract: For a cognitive radar algorithm to be useful, it must run on radar hardware that is flexible. Typically, this has been viewed as requiring a software-defined radio. With the emergence of cheap, digital transceiver systems, such as the Universal Software Radio Peripheral (USRP) from Ettus, these systems are cheaper than ever to produce. This article reports on the development of a USRP-based radar system. Calibration testing for radar cross section is undertaken. The phase noise is characterized as having a standard deviation of 0.02 radians. Detections of commercial aircraft at ranges up to 5.5 km are demonstrated. An adaptive update interval tracking experiment, using the fully adaptive radar framework for cognitive radar, is reported on. In this experiment, a small unmanned aerial vehicle is detected at ranges of up to 300 m. Using the radar equation with a signal loss proportional to R-4, the interpolated maximum detection range is approximately 600 m.
      PubDate: Dec. 1 2019
      Issue No: Vol. 34, No. 12 (2019)
       
 
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