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Home > Archives > Volume 20, No 11 (2022) > Article

DOI: 10.14704/nq.2022.20.11.NQ66229

PARTICLE SWARM OPTIMIZATION ALGORITHM BASED DESIGN AND ANALYSIS OF DIGITAL FIR FILTER USING KAISER WINDOW FUNCTION

Sandeep Kumar, Rajeshwar Singh

Abstract

The digital filter hasa major role in signal processing whichismainly employed in applications forthe reduction of noise in various systems.To send and receive the signal unaltered, there is a need to design filters, which are not only able to remove the noise from the signal, but also signal processing itself does not add noise to the signal. It has been a daunting task for researchers to design digital filters for such systems.Also, there are numerous reasons to use the digital filter and it has forced scientists, engineers, and researchers for designing digital filters with improvised, proficient, and intelligent techniques using emerging modern tools and technology. Out of FIR and IIR Filters, the FIR filters are preferred because of their frequency stability and linearity in phase response. The design ofthe FIR filter has multi-modal optimization challenges. Many optimization algorithms are existing and are suggested by the researcher but it has their advantages and limitations. Further, PSO (Particle Swarm Optimization) algorithm has emerged as an adaptable technique based upon Swarm Intelligence (SI) more specifically particles’ population in the search space. It has a great option for designing anFIR filter. PSO improves the solution characteristicsby providing a unique approach for updating the velocity and position of the swarm. An optimized set of filter coefficientsaregenerated by Particle Swarm Optimization Techniques which give the optimized results in the pass band and stop band.In this research paper, a digital FIR filter is designed using Kaiser Window Function. The designed filter is applied with the PSO algorithm to optimize the design of the filter in MATLAB. The resultsshow that the designed FIR filteris better than the previously designed FIR filter in the context of the frequency spectrum.

Keywords

Particle Swarm Optimization (PSO), Finite Impulse Response Filter (FIR), Swarm Intelligence (SI), Kaiser Window Function.

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