Call for Paper - November 2020 Edition
IJCA solicits original research papers for the November 2020 Edition. Last date of manuscript submission is October 20, 2020. Read More

Design of Linear Phase Low Pass FIR Filter using Particle Swarm Optimization Algorithm

Print
PDF
International Journal of Computer Applications
© 2014 by IJCA Journal
Volume 98 - Number 3
Year of Publication: 2014
Authors:
Neha
Ajay Pal Singh
10.5120/17166-7229

Neha and Ajay Pal Singh. Article: Design of Linear Phase Low Pass FIR Filter using Particle Swarm Optimization Algorithm. International Journal of Computer Applications 98(3):40-44, July 2014. Full text available. BibTeX

@article{key:article,
	author = {Neha and Ajay Pal Singh},
	title = {Article: Design of Linear Phase Low Pass FIR Filter using Particle Swarm Optimization Algorithm},
	journal = {International Journal of Computer Applications},
	year = {2014},
	volume = {98},
	number = {3},
	pages = {40-44},
	month = {July},
	note = {Full text available}
}

Abstract

The everyday broadening field of signal processing has digital filters to play a major role. Linear phase FIR filters are used in vast number of applications due to their nature of phase linearity as well as frequency stability. The traditional non-optimization methods available for filter design suffer from the problem of need for analog to digital conversion and also the inefficient control of frequency response. The conventional gradient based optimization methods are unable to solve non-differential functions and converges to local optimum solution. Thus this paper presents the evolutionary optimization technique of Particle Swarm Optimization (PSO) for the design of linear phase digital low pass (LP) FIR filter. Given the specifications of desired filter to be realized, PSO algorithm results in an optimal coefficient set for linear phase FIR filter approximating the ideal specifications. In this paper PSO algorithm is used with constriction factor approach to solve the multimodal, highly non-linear filter design problem. This method has the property of parameter independence and thus ensuring convergence while fully exploring the solution space. The velocity and position updating rules of original PSO algorithm is used for the design of low pass FIR filter of order 20. The extensive simulation results obtained from the proposed method shows superiority of the algorithm.

References

  • J. G. Proakis and D. G. Manolakis, "Digital Signal Processing-Principles, Algorithms and Applications", New Delhi: Prentice-Hall, 2000.
  • T. W. Parks and C. S. Burrus, "Digital Filter Design". New York:Wiley, 1987.
  • O. Herrmann and W. Schussler, "Design of non recursive digital filters with linear phase", Electronics Letter, vol. 6, pp. 329–330, 1970.
  • T. W. Parks and J. H. McClellan, "Chebyshev approximation for non recursive digital filters with linear phase", IEEE Transactions on Circuit Theory, vol. 19, pp. 189–194, 1972.
  • L. R. Rabiner, "Approximate design relationships for High-pass FIR digital filters", IEEE Transactions on Audio Electroacoustics, vol. 21, pp. 456–460, 1973.
  • M. B. Joaquim and A. S. Lucietto, "A nearly optimum linear-phase digital FIR filters design", Digital Signal Processing, vol. 21, pp. 690–693, 2011.
  • S. Mandal, S. P. Ghoshal, R. Kar and D. Mandal, "Design of optimal linear phase FIR high pass filter using craziness based particle swarm optimization technique", Journal of King Saud University, vol. 24, pp. 83–92, 2012.
  • H. C. Lu and S. T. Tzeng, "Design of arbitrary FIR log filters by genetic algorithm approach", Signal Processing, vol. 80, pp. 497-505, 2000.
  • D. Karaboga, D. H. Horrocks, N. Karaboga and A. Kalinli, "Designing digital FIR filters using Tabu search algorithm", IEEE International Symposium on Circuits and Systems, vol. 4, pp. 2236-2239, 1997.
  • N. Karaboga, "A new design method based on artificial bee colony algorithm for digital IIR filters", Journal of the Franklin Institute, vol. 4, pp. 328–348, 2009.
  • N. Karaboga and B. Cetinkaya, "Design of Digital FIR Filters Using Differential Evolution Algorithm", Circuits System Signal Processing, vol. 25, pp. 649-660D, 2006.
  • J. I. Ababneh and M. H. Bataineh, "Linear phase FIR filter design using particle swarm optimization and genetic algorithms", Digital Signal Processing, vol. 18, pp. 657–668, 2007.
  • J. Kennedy and R. Eberhart, "Particle Swarm Optimization", In Proceeding of IEEE International Conference On Neural Network, vol. 4, pp. 1942-1948, Perth, 1995.
  • B. Luitel and G. K. Venayagamoorthy, "Differential Evolution Particle Swarm Optimization for Digital Filter Design", IEEE Congress on Evolutionary Computation, Hong Kong, pp. 3954-3961,2008.
  • J. Sun, B. Feng and W. B. Xu, "Particle Swarm Optimization with Particles Having Quantum Behaviour", In Proceedings of Congress on Evolutionary Computation, pp. 325-331, 2004.
  • W. Fang, J. Sun, W. Xu, and Jing Liu, "FIR Digital Filters Design Based on Quantum-behaved Particle Swarm Optimization", First International Conference on Innovative Computing, Information and Control, vol. 1, pp. 615-619, Beijing,2006.
  • M. Najjarzadeh and A. Ayatollahi, FIR Digital Filters Design: "Particle Swarm Optimization Utilizing LMS and Minimax Strategies", International symposium on Signal Processing and Information Technology, pp. 129-132, Sarajevo, 2008.
  • M. Clerc and J. Kennedy, "The particle swarm—explosion, stability, and convergence in a multidimensional complex space", IEEE Transaction on Evolutionary Computation, vol. 6, pp. 58–73, 2002.
  • G. Liu, Y. X. Li, and G. He, "Design of Digital FIR Filters Using Differential Evolution Algorithm Based on Reserved Gene", IEEE Congress on Evolutionary Computation, pp. 1-7, Barcelona, 2010.
  • S. Mondal, D. Chakraborty, R. Kar, D. Mandal and S. P. Ghoshal, "Novel Particle Swarm Optimization for Low Pass FIR Filter Design", WSEAS Transactions on Signal Processing, vol. 8, pp. 111-120, 2012.