Skip to main content
Log in

Novel application of FO-DPSO for 2-D parameter estimation of electromagnetic plane waves

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

Parameter estimation of plane waves emitted by sources lying in Fraunhofer zone is one of the active areas of research for last few decades. In this study, Fractional Order Darwinian Particle Swarm Optimization (FO-DPSO) algorithm is designed for direction of arrival and amplitude estimation of plane waves impinging on uniform linear array representing the scenario of monostatic Multiple Input and Multiple Output (MIMO) radar. Approximation theory in the mean square error sense is exploited to develop a fitness function of the problem. Design parameters of the system model are optimized by utilizing the strength of the FO-DPSO algorithm in case of various numbers of non-coherent sources, and analysis is performed in terms of fitness, mean square error, Nash–Sutcliffe efficiency, and computational complexity operators. Worth of the proposed FO-DPSO based optimization mechanism is established by consistently achieving the near to optimal values of performance metrics in all three scenarios for monostatic MIMO radar systems.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6

Similar content being viewed by others

References

  1. Shoaib B, Qureshi IM (2014) A modified fractional least mean square algorithm for chaotic and nonstationary time series prediction. Chin Phys B 23(3):030502

    Article  Google Scholar 

  2. Shoaib B, Qureshi IM (2014) Adaptive step-size modified fractional least mean square algorithm for chaotic time series prediction. Chin Phys B 23(5):050503

    Article  Google Scholar 

  3. Sabatier J, Agrawal OP, Machado JT (2007) Advances in fractional calculus, vol 4. Springer, Dordrecht, p 9

    Book  Google Scholar 

  4. Ortigueira MD, Machado JT, Trujillo JJ, Vinagre BM (2011) Fractional signals and systems. Signal Process 91(3):349

    Article  Google Scholar 

  5. Ortigueira MD, Machado JT (2003) Fractional signal processing and applications. Signal Process 83(11):2285–2286

    Article  Google Scholar 

  6. Machado JT (2016) Fractional dynamics in the Rayleigh’s piston. Commun Nonlinear Sci Numer Simul 31(1):76–82

    Article  Google Scholar 

  7. Lopes AM, Machado JAT (2015) Visualizing control systems performance: a fractional perspective. Adv Mech Eng 7(12):1687814015619831

    Article  Google Scholar 

  8. Ortigueira MD, Machado JT, Rivero M, Trujillo JJ (2015) Integer/fractional decomposition of the impulse response of fractional linear systems. Signal Process 114:85–88

    Article  Google Scholar 

  9. Machado JT (2015) Matrix fractional systems. Commun Nonlinear Sci Numer Simul 25(1):10–18

    Article  Google Scholar 

  10. Chaudhary NI, Raja MAZ, Khan JA, Aslam MS (2013) Identification of input nonlinear control autoregressive systems using fractional signal processing approach. Sci World J 2013:1–13. https://doi.org/10.1155/2013/467276

    Article  Google Scholar 

  11. Chaudhary NI, Raja MAZ, Khan AUR (2015) Design of modified fractional adaptive strategies for Hammerstein nonlinear control autoregressive systems. Nonlinear Dyn 82(4):1811–1830. https://doi.org/10.1007/s11071-015-2279-7

    Article  Google Scholar 

  12. Raja MAZ, Chaudhary NI (2014) Adaptive strategies for parameter estimation of Box–Jenkins systems. IET Signal Process 8(9):968–980. https://doi.org/10.1049/iet-spr.2013.0438

    Article  Google Scholar 

  13. Chaudhary NI, Raja MAZ (2015) Design of fractional adaptive strategy for input nonlinear Box–Jenkins systems. Signal Process 116:141–151. https://doi.org/10.1016/j.sigpro.2015.04.015

    Article  Google Scholar 

  14. Raja MAZ, Chaudhary NI (2015) Two-stage fractional least mean square identification algorithm for parameter estimation of CARMA systems. Signal Process 107:327–339. https://doi.org/10.1016/j.sigpro.2014.06.015

    Article  Google Scholar 

  15. Chaudhary NI, Raja MAZ (2015) Identification of Hammerstein nonlinear ARMAX systems using nonlinear adaptive algorithms. Nonlinear Dyn 79(2):1385–1397. https://doi.org/10.1007/s11071-014-1748-8

    Article  MathSciNet  MATH  Google Scholar 

  16. Shah SM, Samar R, Raja MAZ, Chambers JA (2014) Fractional normalised filtered-error least mean squares algorithm for application in active noise control systems. Electron Lett 50(14):973–975. https://doi.org/10.1049/el.2014.1275

    Article  Google Scholar 

  17. Aslam MS, Raja MAZ (2015) A new adaptive strategy to improve online secondary path modeling in active noise control systems using fractional signal processing approach. Signal Process 107:433–443. https://doi.org/10.1016/j.sigpro.2014.04.012

    Article  Google Scholar 

  18. Shah SM, Samar R, Naqvi SMR, Chambers JA (2014) Fractional order constant modulus blind algorithms with application to channel equalisation. Electron Lett 50(23):1702–1704

    Article  Google Scholar 

  19. Geravanchizadeh M, Ghalami Osgouei S (2014) Speech enhancement by modified convex combination of fractional adaptive filtering. Iran J Electr Electron Eng 10(4):256–266

    Google Scholar 

  20. Osgouei SG, Geravanchizadeh M (2010) Speech enhancement using convex combination of fractional least-mean-squares algorithm. In: Telecommunications (IST), 2010 5th international symposium on, IEEE, pp 869–872

  21. Yasin M, Akhtar P (2012) Performance analysis of bessel beamformer with LMS algorithm for smart antenna array. In: Open source systems and technologies (ICOSST), 2012 international conference on, IEEE, pp 1–5

  22. Petráš I (2009) Fractional-order feedback control of a DC motor. J Electr Eng 60(3):117–128

    MathSciNet  Google Scholar 

  23. Tarasov VE (2011) Fractional dynamics: applications of fractional calculus to dynamics of particles, fields and media. Springer, Berlin

    Google Scholar 

  24. Baleanu D, Güvenç ZB, Machado JT (eds) (2010) New trends in nanotechnology and fractional calculus applications. Springer, New York, p C397

    Google Scholar 

  25. Ortigueira MD (2011) Fractional calculus for scientists and engineers, vol 84. Springer, Dordrecht

    Book  MATH  Google Scholar 

  26. Liu J, Wang X, Zhou W (2016) Covariance vector sparsity-aware DOA estimation for monostatic MIMO radar with unknown mutual coupling. Signal Process 119:21–27

    Article  Google Scholar 

  27. Bahiraei M, Hangi M (2016) Numerical investigation and optimization of flow and thermal characteristics of nanofluid within a chaotic geometry. Adv Powder Technol 27(1):184–192

    Article  Google Scholar 

  28. Song X, Wang J, Wang B (2013) Robust blind adaptive beamforming under double constraints. Neural Comput Appl 22(2):295–302

    Article  Google Scholar 

  29. Bahiraei M, Hangi M, Saeedan M (2015) A novel application for energy efficiency improvement using nanofluid in shell and tube heat exchanger equipped with helical baffles. Energy 93:2229–2240

    Article  Google Scholar 

  30. Liu Q, Wang X (2016) Direction of arrival estimation via reweighted l_1 norm penalty algorithm for monostatic MIMO radar. Multidimens Syst Signal Process 1–12. https://doi.org/10.1007/s11045-016-0392-5

  31. Bahiraei M, Khosravi R, Heshmatian S (2017) Assessment and optimization of hydrothermal characteristics for a non-Newtonian nanofluid flow within miniaturized concentric-tube heat exchanger considering designer’s viewpoint. Appl Therm Eng 123:266–276

    Article  Google Scholar 

  32. Solteiro Pires EJ, Machado TJA, de Moura Oliveira PB (2014) Fractional particle swarm optimization mathematical methods in engineering. Springer, Netherlands, pp 47–56

    Google Scholar 

  33. Gao Z, Wei J, Liang C, Yan M (2014) Fractional-order particle swarm optimization. In: The 26th Chinese control and decision conference (2014 CCDC), IEEE, pp 1284–1288

  34. Solteiro Pires EJ, Tenreiro JA, de Machado PB, Oliveira Moura, Cunha JB, Mendes L (2010) Particle swarm optimization with fractional-order velocity. Nonlinear Dyn 61(1–2):295–301

    Article  MATH  Google Scholar 

  35. Couceiro MS, Rocha RP, Ferreira NF, Machado JT (2012) Introducing the fractional-order Darwinian PSO. Signal Image Video Process 6(3):343–350

    Article  Google Scholar 

  36. Couceiro MS, Martins FM, Rocha RP, Ferreira NM, Sivasundaram S (2012) Introducing the fractional order robotic Darwinian PSO. In: AIP conference proceedings-American institute of physics, vol 1493, no. 1, p 242

  37. Ghamisi P, Couceiro MS, Fauvel M, Benediktsson JA (2013) Spectral-spatial classification based on integrated segmentation. In: 2013 IEEE international geoscience and remote sensing symposium-IGARSS, IEEE, pp 1458–1461

  38. Ghamisi P, Couceiro MS, Benediktsson JA (2015) A novel feature selection approach based on FODPSO and SVM. IEEE Trans Geosci Remote Sens 53(5):2935–2947

    Article  Google Scholar 

  39. Couceiro MS, Machado JT, Rocha RP, Ferreira NM (2012) A fuzzified systematic adjustment of the robotic Darwinian PSO. Robot Auton Syst 60(12):1625–1639

    Article  Google Scholar 

  40. Ghamisi P, Couceiro MS, Benediktsson JA (2013) Classification of hyperspectral images with binary fractional order Darwinian PSO and random forests. In: SPIE remote sensing, International Society for Optics and Photonics, pp 88920S–88920S

  41. Krim H, Viberg M (1996) Two decades of array signal processing research: the parametric approach. IEEE Signal Process Mag 13(4):67–94

    Article  Google Scholar 

  42. Zaman F, Qureshi IM, Munir F, Khan ZU (2014) Four-dimensional parameter estimation of plane waves using swarming intelligence. Chin Phys B 23(7):078402

    Article  Google Scholar 

  43. Guo R, Li W, Zhang Y, Chen Z (2016) New direction of arrival estimation of coherent signals based on reconstructing matrix under unknown mutual coupling. J Appl Remote Sens 10(1):015013

    Article  Google Scholar 

  44. Liberti JC, Rappaport TS (1999) Smart antennas for wireless communications: IS-95 and third generation CDMA applications. Prentice Hall PTR, Englewood Cliffs

    Google Scholar 

  45. Moore AH, Evers C, Naylor PA, Moore AH, Evers C, Naylor PA (2017) Direction of arrival estimation in the spherical harmonic domain using subspace pseudointensity vectors. IEEE/ACM Trans Audio Speech Lang Process (TASLP) 25(1):178–192

    Article  Google Scholar 

  46. Dey N, Ashour AS, Shi F, Sherratt RS (2017) Wireless capsule gastrointestinal endoscopy: direction of arrival estimation based localization survey. IEEE Rev Biomed Eng PP:1

    Google Scholar 

  47. Khan ZU, Naveed A, Qureshi IM, Zaman F (2011) Independent null steering by decoupling complex weights. IEICE Electron Express 8(13):1008–1013

    Article  Google Scholar 

  48. Spors S, Rettberg T, Winter F (2017) A comparison of modal and spatially matched-filter beamforming for rigid spherical microphone arrays in the context of data-based binaural synthesis. J Acoust Soc Am 141(5):3855

    Article  Google Scholar 

  49. Zaman F, Qureshi IM, Naveed A, Khan ZU (2012) Joint estimation of amplitude, direction of arrival and range of near field sources using memetic computing. Prog Electromagn Res C 31:199–213

    Article  Google Scholar 

  50. Tan J, Nie Z, Wen D (2017) Low complexity MUSIC-based direction-of-arrival algorithm for monostatic MIMO radar. Electron Lett 53(4):275–277

    Article  Google Scholar 

  51. Zheng G, Tang J (2016) Two-dimensional DOA estimation for monostatic MIMO radar with electromagnetic vector received sensors. Int J Antennas Propag 2016:10

    Google Scholar 

  52. Zhang X, Huang Y, Chen C, Li J, Xu D (2012) Reduced-complexity Capon for direction of arrival estimation in a monostatic multiple-input multiple-output radar. IET Radar Sonar Navig 6(8):796–801

    Article  Google Scholar 

  53. De Oliveira EC, Machado JAT (2014) A review of definitions for fractional derivatives and integral. Math Probl Eng 2014, ID.238459

  54. Davison M, Essex C (1998) Fractional differential equations and initial value problems. Math Sci 23(2):108–116

    MathSciNet  MATH  Google Scholar 

  55. Tenreiro Machado JA, Solteiro Pires EJ, Couceiro MS (2014) Reply to: comments on “particle swarm optimization with fractional-order velocity”. Nonlinear Dyn 77(1–2):435–443

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Ata Ur Rehman.

Ethics declarations

Conflict of interest

All the authors of the manuscript declared that there are no potential conflicts of interest.

Human and animal rights statements

All the authors of the manuscript declared that there is no research involving human participants and/or animal.

Informed consent

All the authors of the manuscript declared that there is no material that required informed consent.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Akbar, S., Zaman, F., Asif, M. et al. Novel application of FO-DPSO for 2-D parameter estimation of electromagnetic plane waves. Neural Comput & Applic 31, 3681–3690 (2019). https://doi.org/10.1007/s00521-017-3318-8

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-017-3318-8

Keywords

Navigation