New Design Method of UWB Microstrip Filters Using Adaptive Genetic Algorithms with Defected Ground Structures

Amir Reza Dastkhosh,Gholamreza Dadashzadeh,Mohammad Hossein Sedaaghi

doi:10.1155/2010/671515

Abstract

The effects of adaptive genetic algorithms (AGAs) and defected ground structures (DGSs) on performance optimization of tapered microstrip filter are investigated. The proposed structure achieves an ultra wide stopband with high attenuation within a small surface area, as well as 45% smaller size, in comparison with conventional filters. The parameters of the filter are optimized using in-home AGA code. In the proposed AGA algorithm, the crossover and mutation probabilities are adaptively changed according to the value of individual fitness. Then by utilizing the proposed DGS, a compact S-band lowpass filter with ultra-wide spurious free window is obtained. The proposed filter achieves an insertion loss of 0.8 dB from DC up to 4 GHz and 21 dB rejection in the stopband from 4.3 up to 60 GHz. The fabricated and measured results exhibit good agreement with the simulated results. They demonstrate that combining AGA and DGS yields best possible response for this group of filters.

Highlights

High-performance microwave filters with minimized size and weight are playing an important role for design and fabrication of high-efficiency miniaturized microwave systems
We have proposed a high-performance and compact lowpass filter structure with ultra-wide stopband using adaptive genetic algorithms and new defected ground structures (DGSs) configurations
The DGS elements are cascaded in order to realize wider stopband with very sharp edge filter

Summary

Introduction

High-performance microwave filters with minimized size and weight are playing an important role for design and fabrication of high-efficiency miniaturized microwave systems. Nonuniform transmission lines (NUTLs) play an important role in microwave circuits. Genetic algorithms are widely employed in various fields such as optimal engineering designs. They have been successfully applied to finding the global optimum in a variety of unimodal domains. Parameter control methods are classified as deterministic and adaptive. Deterministic systems employ fixed, predefined parameters for GA. Adaptive control uses feedback from the search process to find out how the parameter values change [8, 9]

Methods

Results

Conclusion