Hostname: page-component-586b7cd67f-2plfb Total loading time: 0 Render date: 2024-11-28T16:05:49.563Z Has data issue: false hasContentIssue false
  • English
  • Français

The optimal design and analysis of wideband second-order microwave integrator

Published online by Cambridge University Press:  08 February 2019

Usha Gautam Open the ORCID record for Usha Gautam [Opens in a new window]  and
Tarun Kumar Rawat
Usha Gautam*
Affiliation:
Department of ECE, Netaji Subhas Institute of Technology, New-Delhi, India
Tarun Kumar Rawat
Affiliation:
Department of ECE, Netaji Subhas Institute of Technology, New-Delhi, India
*
Author for correspondence: Email: Usha Gautam, [email protected]
Get access Rights & Permissions [Opens in a new window]

Abstract

The implementation of stable, accurate, and wideband second-order microwave integrators (SOMIs) is presented in this paper. These designs of SOMIs are obtained by using different combinations of transmission line sections and shunt stubs in cascading. Particle swarm optimization (PSO), cuckoo search algorithm (CSA), and gravitational search algorithm (GSA) are applied to obtain the optimal values of the characteristic impedances of these line elements to approximate the magnitude response of ideal second-order integrator (SOI). The performance measure criteria for the proposed SOMIs are carried out based on magnitude response, absolute magnitude error, phase response, convergence rate, pole-zero plot, and improvement graph. The simulation results and statistical analysis demonstrate that GSA surpasses the PSO and CSA to approximate the ideal SOI in all state-of-the-art, that is eligible for wide-band microwave integrator. The designed SOMI is compact in size and suitable to cover microwave applications. The magnitude errors for the proposed SOMIs GSA based are as low as 4.9954 and 3.6573, respectively. The structure of the designed SOMI is implemented in the form of microstrip line on RT/Duroid substrate with dielectric constant 2.2 and having height 0.762 mm. The simulated and measured magnitude result agrees well with the ideal one in the frequency range of 3–15 GHz.

Keywords

Equal lengthgravitational search algorithmline elementsmicrowave integratorsecond order
Type
Research Papers
Information
International Journal of Microwave and Wireless Technologies , Volume 11 , Issue 3 , April 2019 , pp. 227 - 236
Copyright
Copyright © Cambridge University Press and the European Microwave Association 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

References

1.Oppenheim, AV and Shafer, RW (1989) Discrete-Time Signal Processing. New Jersey: Prentice-Hall.Google Scholar
2.Ngo, NQ (2006) A new approach for the design of wideband digital integrator and differentiator. IEEE Transactions on Circuits and Systems II: Express Briefs 53, 936–940.Google Scholar
3.Gupta, M, Jain, M and Kumar, B (2010) Novel class of stable wideband recursive digital integrators and differentiators. IET Signal Processing 4, 560–566.Google Scholar
4.Upadhyay, DK and Singh, RK (2011) Recursive wideband digital differentiator and integrator. Electronics Letters 47, 647–648.Google Scholar
5.Upadhyay, DK (2012) Class of recursive wideband digital differentiators and integrators. Radioengineering 21, 904–910.Google Scholar
6.Garg, K and Upadhyay, DK (2017) Design of second-order recursive digital integrators with matching phase and magnitude response. Radioengineering 26, 376–386.Google Scholar
7.Skolink, MI (1980) Introduction to Radar Systems. New York: McGraw-Hill.Google Scholar
8.Hsue, CW, Tsai, LC and Kan, ST (2006) Implementation of a trapezoidal rule microwave integrator. Microwave and Optical Technology Letters 48, 822–825.Google Scholar
9.Hsue, C -W, Tsai, L -C, Tsai, Y-H. (2006) Time-constant control of microwave integrators using transmission lines. IEEE Transactions on Microwave Theory and Techniques 54, 1043–1047.Google Scholar
10.Tsai, L-C and Wu, Y-T (2008) Time-constant control analysis of microwave differentiators. IET Microwaves, Antennas & Propagation 3, 1044–1050.Google Scholar
11.Tsai, LC and Fang, HS (2011) Design and implementation of second-order microwave integrators. Microwave and Optical Technology Letters 53, 1983–1986.Google Scholar
12.Upadhyay, DK and Singh, RK (2013) A simple approach to control the time constant of microwave integrator. IJMOT 8, 23–27.Google Scholar
13.Gautam, U, Upadhyay, DK and Rawat, TK (2016) New designs of first order microwave integrator. IEEE International Conference on Signal Processing (SPIN), India, 285–289.Google Scholar
14.Gupta, M and Upadhyay, DK (2017) New design of second order microwave integrator. IEEE International Conference on Innovative Mechanisms for Industry Applications (ICIMIA), India, 556–560.Google Scholar
15.Kennedy, J and Eberhart, R (1995) Particle Swarm optimization. Proceedings of IEEE International Conference Neural Network, 4, 1942–48.Google Scholar
16.Shi, JY and Eberhart, RC (1999) Empirical study of particle swarm optimization. Proceedings of the Congress on Evolutionary Computation (CEC99), Washington, DC, USA, 3, 1945–1950.Google Scholar
17.Yang, XS and Deb, S (2009) Cuckoo search via Lvy flights. Proceedings of world congress on nature and biologically inspired computing, USA IEEE Publications, 210–214.Google Scholar
18.Yang, XS and Deb, S (2014) Cuckoo search: recent advances and applications. Neural Computing & Applications 24, 169–74.Google Scholar
19.Kumar, M and Rawat, TK (2015) Optimal fractional delay-IIR filter design using cuckoo search algorithm. ISA Transaction 59, 39–54.Google Scholar
20.Aggarwal, A, Rawat, TK, Kumar, M and Upadhyay, DK (2016) Efficient design of digital FIR differentiator using L 1-method. Radioengineering 25, 86–92.Google Scholar
21.Aggarwal, A, Kumar, M, Rawat, TK and Upadhyay, DK (2017) Optimal design of 2-D FIR digital differentiator uing L1-norm based cuckoo-search algorithm. Multidimensional Systems and Signals Processing 28, 1569–87.Google Scholar
22.Dhabal, S and Venkateswaran, P (2017) An efficient gbest-guided Cuckoo Search algorithm for higher order two channel filter bank design. Swarm and Evolutionary Computation 33, 68–84.Google Scholar
23.Barsainya, R, Aggarwal, A and Rawat, T (2018) Optimal design of minimum multiplier lattice wave digital lowpass filter using metaheuristic techniques. IET Signal Processing 12, 700–712.Google Scholar
24.Hong, JS and Lancanter, MJ (2001) Microstrip Filters for RF/Microwave Applications. NewYork: Wiley.Google Scholar
25.Rashedi, E, Nezamabadi, S and Saryazdi, S (2009) GSA: a gravitational search algorithm. Information Sciences 179, 2232–48.Google Scholar
26.Newton Isaac, . In Experimental Philosophy Particular Propositions are Inferred from The Phenomena and Afterwards Rendered General by Induction, 3rd ed.: Andrew Motte's English Translation published 1729.Google Scholar
27.Rashedi, E, Nezamabadi, S and Saryazdi, S (2011) Filter modelling using gravitational search algorithm. Engineering Applications of Artificial Intelligence's 24, 117–122.Google Scholar
28.Saha, SK, Kar, R, Mandal, D and Ghoshal, SP (2014) Gravitation search algorithm: application to the optimal IIR filter design. Journal of King Saud university-Engineering Sciences 26, 69–81.Google Scholar
29.Siddiquea, N and Adelib, H (2016) Application of gravitational search algorithm in engineering. Journal of Civil Engineering and Management 22, 981–990.Google Scholar
30.Bahl, I. (2003) Lumped Elements for RF and Microwave Circuits. London: Boston.Google Scholar