193
Views
1
CrossRef citations to date
0
Altmetric
Research Article

A boomerang-shaped UWB antenna for spectrum sensing

, , , &
Pages 977-993 | Received 30 Jul 2021, Accepted 17 Oct 2021, Published online: 28 Oct 2021
 

Abstract

A boomerang-shaped ultra wide band (UWB) planar antenna has been presented for spectrum sensing applications. The presented antenna has been built upon a narrow band isotropic antenna design. In order to achieve a wider bandwidth, an exponential profile (similar to log spiral) is used. Simulation results show that the antenna has an impedance bandwidth of 163% (1.3–12 GHz) for VSWR≤2 with good radiation properties. In order to quantify the antenna performance, antenna radiation pattern and efficiency have been compared with previously reported designs. It is shown that the proposed design is able to achieve nearly omnidirectional patterns in more than one planes while achieving a maximum radiation efficiency of 92%. The overall dimensions of the proposed design are 193.6mm×196.5 mm. The prototype of the proposed antenna is manufactured using FR4 substrate (εr=4.4, h = 1.6 mm).

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Notes on contributors

Ubaid ur Rahman Qureshi

Ubaid Ur Rahman Qureshi received the B.E. degree in Electrical Engineering from the HITEC University, Taxila, Pakistan, in 2016, and the M.S. degree (Hons.) in Electrical Engineering with specialization in RF and microwave from the Institute of Space Technology, Islamabad, Pakistan, in 2020. He is currently doing PhD at the Beijing Institute of Technology, China. His research interests include RF circuits, Antenna design, and Metasurface.

Moazam Maqsood

Moazam Maqsood was born in Faisalabad, Pakistan, in 1983. He received the B.Sc. degree in communication systems engineering from the Institute of Space Technology, Islamabad, Pakistan, in 2006, and the MS degree in microwave engineering and wireless subsystem design from the University of Surrey, Guildford, U.K, in 2009, and the Ph.D. degree in integrated antennas and arrays for GNSS from University of Surrey in 2013. He is currently an Assistant Professor in the Department of Electrical Engineering, Institute of Space Technology, Islamabad, Pakistan.

Muhammad Amin

Muhammad Amin received the B.E. degree in avionics from the PAF College of Aeronautical Engineering, NED University, Karachi, Pakistan, in 1988, the masters degree in electrical engineering with specialization in high-frequency techniques from Ruhr University, Bochum, Germany, in 1998, and the Ph.D. degree from Queens University Belfast (QUB), Belfast, U.K., in 2006. He was a consultant with TDK Electronics to develop phased array antenna for automotive collision avoidance radar. He was a Research Fellow with QUB for approximately one year. He was the head of the Antenna and EMI/EMC labs at Satellite Research and Development Centre, Lahore (SRDC-L), Pakistan, where he was involved in developing monopoles tracking system for satellite and EMI/EMC space qualification tests of the satellite communications system. Since 2015, he has been a Professor with Institute of Space Technology (IST). His research interests include the development of antennas for radar and cellular communication systems, novel techniques for modulation, and RCS reduction. His research work has resulted in over 70 publications in major journals and refereed national and international conferences. He is the inventor of a lowest profile dual polarized antenna. He is mentioned in Marquis Who is Who in the World 2008 edition published in USA.

Abdur Rahman Maud

Abdur Rahman has been working as an Assistant Professor in the Electrical Engineering department at IST since 2015. He received his MS and PhD degree in Electrical and Computer Engineering from Purdue University, USA in 2012 and 2015, respectively. Earlier, he received his BSc degree in Electrical Engineering from University of Engineering and Technology, Lahore in 2008. His research interests lie in the field of signal processing in general including radar/ array signal processing, exploitation of sparsity in signal models and application of machine learning to various problems.

Nosherwan Shoaib

Nosherwan Shoaib (Senior Member, IEEE) received the double masters degree in electronics engineering and the Ph.D. degree in RF and microwave measurement engineering from the Politecnico di Torino, Italy, in September 2011 and February 2015, respectively. During his Ph.D. studies, he has actively participated as a Researcher in the European Metrology Research Programme (EMRP) Project SIB62 Metrology for New Electrical Measurement Quantities in High Frequency Circuits. Afterward, he served two terms as a Postdoctoral Research Fellow with the National Institute of Metrological Research (INRIM), Italy, and the Khalifa University of Science and Technology, United Arab Emirates (UAE). He joined the Research Institute for Microwave and Millimeter-Wave Studies (RIMMS), School of Electrical Engineering and Computer Sciences (SEECS), National University of Sciences and Technology (NUST), Islamabad, in September 2016, as an Assistant Professor. He teaches RF and microwave-related courses to electrical engineering students. He contributed to two patents, one book, and more than 50 leading international technical journal, peer-reviewed conference articles and technical reports. His current research interests include design and development of 5G MIMO antennas, RF energy harvesting, the Internet of Things (IoT), RF metrology, and microwave active circuits.

Log in via your institution

Log in to Taylor & Francis Online

PDF download + Online access

  • 48 hours access to article PDF & online version
  • Article PDF can be downloaded
  • Article PDF can be printed
USD 61.00 Add to cart

Issue Purchase

  • 30 days online access to complete issue
  • Article PDFs can be downloaded
  • Article PDFs can be printed
USD 561.00 Add to cart

* Local tax will be added as applicable

Related Research

People also read lists articles that other readers of this article have read.

Recommended articles lists articles that we recommend and is powered by our AI driven recommendation engine.

Cited by lists all citing articles based on Crossref citations.
Articles with the Crossref icon will open in a new tab.