Adaptive Radar Resource Management

Peter Moo

ISBN 10: 0128029021 / ISBN 13: 9780128029022

Edité par Elsevier Science & Technology, Academic Press, 2015

A propos de cet article

nach der Bestellung gedruckt Neuware - Printed after ordering - Radar Resource Management (RRM) is vital for optimizing the performance of modern phased array radars, which are the primary sensor for aircraft, ships, and land platforms. Adaptive Radar Resource Management gives an introduction to radar resource management (RRM), presenting a clear overview of different approaches and techniques, making it very suitable for radar practitioners and researchers in industry and universities. Coverage includes: RRM's role in optimizing the performance of modern phased array radars The advantages of adaptivity in implementing RRM The role that modelling and simulation plays in evaluating RRM performance Description of the simulation tool Adapt_MFR Detailed descriptions and performance results for specific adaptive RRM techniques.

N° de réf. du vendeur 9780128029022

Détails bibliographiques

• Titre: Adaptive Radar Resource Management
• Éditeur: Elsevier Science & Technology, Academic Press
• Date de parution: 2015
• Langue: anglais
• ISBN à 10 chiffres: 0128029021
• ISBN à 13 chiffres: 9780128029022
• Reliure: Taschenbuch
• État de l'article: Neu

A propos de ce titre

Synopsis

Radar Resource Management (RRM) is vital for optimizing the performance of modern phased array radars, which are the primary sensor for aircraft, ships, and land platforms. Adaptive Radar Resource Management gives an introduction to radar resource management (RRM), presenting a clear overview of different approaches and techniques, making it very suitable for radar practitioners and researchers in industry and universities.

Coverage includes:

• RRM's role in optimizing the performance of modern phased array radars
• The advantages of adaptivity in implementing RRM
• The role that modelling and simulation plays in evaluating RRM performance
• Description of the simulation tool Adapt_MFR
• Detailed descriptions and performance results for specific adaptive RRM techniques

À propos des auteurs

Peter W. Moo received a B.Sc. in mathematics and engineering from Queen's University at Kingston and a M.S.E and a Ph.D. in electrical engineering: systems from the University of Michigan, where he was a National Science Foundation Graduate Fellow. In 1997 he was a visiting researcher at General Electric Corporate Research and Development, Schenectady, NY. From 1998 to 1999 he was a postdoctoral fellow in the Department of Computer Science at the University of Western Ontario. Since 1999, he has been a defence scientist at Defence R&D Canada, where he is currently Leader of the Wide Area Surveillance Radar Group in the Radar Sensing & Exploitation Section. His research interests include MIMO radar, space-time adaptive processing, and radar resource management.

Zhen Ding received a bachelor degree, a M.S. degree and a Ph.D. degree in automatic control from North-western Polytechnic University (NPU), Xi'an, China, in 1986, 1989 and 1995. He was a lecturer and researcher at NPU from 1989 to 1995. Then he was a post-doctoral fellow in Wright State University, Dayton, Ohio, USA (1996-1997), and in McMaster University, Hamilton, Ontario, Canada (1998). He joined Raytheon Canada in Jan. 1999, where he was involved with the design, implementation and testing of radar trackers for marine radars and High Frequency Surface Wave Radars (HFSWR). Also involved during that time were tracker enhancements for Air Traffic Control (ATC) radar under wind farm environments. He has been with Radar Sensing and Exploitation Section, Defence R&D Canada, as a defence scientist since May 2007, where he conducts research on target tracking and radar resource management. He is one of the founding members of the Canadian Tracking and Fusion Group (CTFG).