Robust Estimation of IIR System’s Parameter Using Adaptive Particle Swarm Optimization Algorithm

  • Meera DashEmail author
  • Trilochan Panigrahi
  • Renu Sharma
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 711)


This paper introduces a novel method of robust parameter estimation of IIR system. When training signal contains strong outliers, the conventional squared error-based cost function fails to provide desired performance. Thus, a computationally efficient robust Hubers cost function is used here. As we know that the IIR system falls in local minima, gradient-based algorithm cannot be used. Therefore, the parameters of the IIR system are estimated using adaptive particle swarm optimization algorithm with Hubers cost function. The simulation results show that the proposed algorithm provides better performance than Wilcoxon norm-based robust algorithm and conventional error squared based PSO algorithm.


IIR system Impulsive noise Robust estimation Wilcoxon norm Hubers cost function Adaptive particle swarm optimization 


  1. 1.
    J. I. Ababneh and M. H. Bataineh, “Linear phase fir filter design using particle swarm optimization and genetic algorithms,” Digital Signal Processing, vol. 18, no. 4, pp. 657–668, 2008.CrossRefGoogle Scholar
  2. 2.
    M. Nayak, T. Panigrahi, and R. Sharma, “Distributed estimation using multi-hop adaptive diffusion in sparse wireless sensor networks,” in International Conference on Microwave, Optical and Communication Engineering (ICMOCE), Dec 2015, pp. 318–321.Google Scholar
  3. 3.
    J. J. Shynk, “Adaptive IIR filtering,” IEEE ASSP Magazine, vol. 6, no. 2, pp. 4–21, April 1989.Google Scholar
  4. 4.
    B. Widrow and S. D. Strearns, Adaptive Signal Processing. Englewood Cliffs, NJ:Prentice-Hall, 1985.Google Scholar
  5. 5.
    L. Xue, Z. Rongchun, and W. Qing, “Optimizing the design of IIR filter via genetic algorithm,” in Neural Networks and Signal Processing, 2003. Proceedings of the 2003 International Conference on, vol. 1, Dec. 2003, pp. 476–479 Vol. 1.Google Scholar
  6. 6.
    G. Panda, P. M. Pradhan, and B. Majhi, “IIR system identification using cat swarm optimization,” Expert Systems with Applications, vol. 38, no. 10, pp. 12671–12683, 2011.CrossRefGoogle Scholar
  7. 7.
    S. Saha, R. Kar, D. Mandal, and S. Ghoshal, “Harmony search algorithm for infinite impulse response system identification,” Computers and Electrical Engineering, vol. 40, no. 4, pp. 1265–1285, 2014.CrossRefGoogle Scholar
  8. 8.
    S. R. Kim and A. Efron, “Adaptive robust impulsive noise filtering,” IEEE Transactions on Signal Processing, vol. 43, no. 8, pp. 1855–1866, Aug. 1995.Google Scholar
  9. 9.
    B. Majhi, G. Panda, and B. Mulgrew, “Robust identification and prediction using wilcoxon norm and particle swarm optimization,” in 17th European Signal Processing Conference, Aug 2009, pp. 1695–1699.Google Scholar
  10. 10.
    T. Panigrahi, G. Panda, and B. Mulgrew, “Error saturation nonlinearities for robust incremental LMS over wireless sensor networks,” ACM Trans. on Sensor Network, vol. 11, no. 2, pp. 27:1–27:20, Dec. 2014.CrossRefGoogle Scholar
  11. 11.
    T. Panigrahi, B. Mulgrew, and B. Majhi, “Robust distributed linear parameter estimation in wireless sensor network,” in 2011 International Conference on Energy, Automation and Signal, Dec 2011, pp. 1–5.Google Scholar
  12. 12.
    Y. Zhong, Y. Deng, and A. K. Jain, “Keystroke dynamics for user authentication,” in 2012 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops, June 2012, pp. 117–123.Google Scholar
  13. 13.
    K. Q. Weinberger and L. K. Saul, “Distance metric learning for large margin nearest neighbor classification,” Journal of Machine Learning Research, vol. 10, pp. 207–244, Jun. 2009.Google Scholar
  14. 14.
    M. Kstinger, M. Hirzer, P. Wohlhart, P. M. Roth, and H. Bischof, “Large scale metric learning from equivalence constraints,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, June 2012, pp. 2288–2295.Google Scholar
  15. 15.
    T. Panigrahi, M. Panda, and G. Panda, “Fault tolerant distributed estimation in wireless sensor networks,” Journal of Network and Computer Applications, vol. 69, pp. 27–39, 2016.CrossRefGoogle Scholar
  16. 16.
    J. Robinson and Y. Rahmat-Samii, “Particle swarm optimization in electromagnetics,” IEEE Transactions on Antennas and Propagation, vol. 52, no. 2, pp. 397–407, Feb. 2004.Google Scholar
  17. 17.
    A. Nickabadi, M. M. Ebadzadeh, and R. Safabakhsh, “A novel particle swarm optimization algorithm with adaptive inertia weight,” Applied Soft Computing, vol. 11, no. 4, pp. 3658–3670, 2011.CrossRefGoogle Scholar
  18. 18.
    T. Panigrahi, D. H. Rao, G. Panda, B. Mulgrew, and B. Majhi, “Maximum likelihood DOA estimation in distributed wireless sensor network using adaptive particle swarm optimization,” in the Proc. of ACM International Conference on Communication, Computing and Security (ICCCS2011), Feb. 2011, pp. 134–136.Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of ECEITER Siksha ‘O’ Anusandhan UniversityBhubaneswarIndia
  2. 2.Department of ECENational Institute of Technology GoaPondaIndia
  3. 3.Department of EEITER Siksha ‘O’ Anusandhan UniversityBhubaneswarIndia

Personalised recommendations