http://iet.metastore.ingenta.com
1887

Hardware implementation of the quasi-maximum likelihood estimator core for polynomial phase signals

Hardware implementation of the quasi-maximum likelihood estimator core for polynomial phase signals

For access to this article, please select a purchase option:

Buy eFirst article PDF
£12.50
(plus tax if applicable)
Buy Knowledge Pack
10 articles for £75.00
(plus taxes if applicable)

IET members benefit from discounts to all IET publications and free access to E&T Magazine. If you are an IET member, log in to your account and the discounts will automatically be applied.

Learn more about IET membership 

Recommend Title Publication to library

You must fill out fields marked with: *

Librarian details
Name:*
Email:*
Your details
Name:*
Email:*
Department:*
Why are you recommending this title?
Select reason:
 
 
 
 
 
IET Circuits, Devices & Systems — Recommend this title to your library

Thank you

Your recommendation has been sent to your librarian.

Flexible, multiple-clock-cycle, hardware design for the quasi-maximum likelihood (QML) algorithm core realisation for the polynomial phase signals (PPSs) estimation is proposed. The QML algorithm significantly outperforms existing PPS estimators in terms of accuracy. However, its practical applications require efficient software and hardware systems. The main challenges in the proposed hardware development with respect to existing systems for time–frequency (TF) analysis are realisation of TF representation based instantaneous frequency estimator, the polynomial regression, and phase extraction. The developed design is tested on a PPS corrupted by a white Gaussian noise and verified by a field programmable gate array circuit design. All implementation and verification details are provided along with the comparison of the results achieved by hardware and software implementations.

References

    1. 1)
      • 1. Djurović, I., Simeunović, M., Lutovac, B.: ‘Are genetic algorithms useful for the parameter estimation of FM signals’, Digit. Signal Process., 2012, 22, (6), pp. 11371144.
    2. 2)
      • 2. Djurović, I., Simeunović, M., Wang, P.: ‘Cubic phase function: a simple solution for polynomial phase signal analysis’, Signal Process., 2017, 135, pp. 4866.
    3. 3)
      • 3. McKilliam, R., Quinn, B., Clarkson, I., et al: ‘Polynomial phase estimation by least squares phase unwrapping’, IEEE Trans. Signal Process., 2014, 62, (8), pp. 19621975.
    4. 4)
      • 4. Friedlander, B., Francos, J.M.: ‘Estimation of amplitudes and phase parameters of multicomponent signals’, IEEE Trans. Signal Process., 1995, 43, (4), pp. 917926.
    5. 5)
      • 5. Peleg, S., Porat, B.: ‘Estimation and classification of polynomial phase signals’, IEEE Trans. Inf. Theory, 1991, 37, (2), pp. 422430.
    6. 6)
      • 6. Barbarossa, S., Petrone, V.: ‘Analysis of polynomial-phase signals by the integrated generalized ambiguity function’, IEEE Trans. Signal Process., 1997, 45, (2), pp. 316327.
    7. 7)
      • 7. Pham, D.S., Zoubir, A.M.: ‘Analysis of multicomponent polynomial phase signals’, IEEE Trans. Signal Process., 2007, 55, (1), pp. 5665.
    8. 8)
      • 8. Porat, B., Friedlander, B.: ‘Asymptotic statistical analysis of the high-order ambiguity function for parameter estimation of polynomial-phase signals’, IEEE Trans. Inf. Theory, 1996, 42, (3), pp. 9951001.
    9. 9)
      • 9. Barbarossa, S., Scaglione, A., Giannakis, G.B.: ‘Product high-order ambiguity function for multicomponent polynomial phase signal modeling’, IEEE Trans. Signal Process., 1998, 46, (3), pp. 691708.
    10. 10)
      • 10. O'Shea, P.: ‘A new technique for instantaneous frequency rate estimation’, IEEE Signal Process. Lett., 2002, 9, (8), pp. 251252.
    11. 11)
      • 11. Zhou, T.G., Wang, Y.: ‘Exploring lag diversity in the high-order ambiguity function for polynomial phase signals’, IEEE Signal Process. Lett., 1997, 4, (8), pp. 240242.
    12. 12)
      • 12. O'Shea, P.: ‘A fast algorithm for estimating the parameters of a quadratic FM signal’, IEEE Trans. Signal Process., 2004, 52, (2), pp. 385393.
    13. 13)
      • 13. Farquharson, M., O'Shea, P.: ‘Extending the performance of the cubic phase function algorithm’, IEEE Trans. Signal Process., 2007, 55, (10), pp. 47674774.
    14. 14)
      • 14. Djurović, I., Simeunović, M., Djukanović, S., et al: ‘A hybrid CPF-HAF estimation of polynomial-phase signals: detailed statistical analysis’, IEEE Trans. Signal Process., 2012, 60, (10), pp. 50105023.
    15. 15)
      • 15. Simeunović, M., Djurović, I.: ‘CPF-HAF estimator of polynomial-phase signals’, IET Electron. Lett., 2011, 47, (17), pp. 965966.
    16. 16)
      • 16. O'Shea, P.: ‘An iterative algorithm for estimating the parameters of polynomial phase signals’. Proc. of Int. Symp. on Signal Processing and its Applications (ISSPA), August 1996, vol. 2, pp. 730731.
    17. 17)
      • 17. Djurović, I., Wang, P., Ioana, C.: ‘Parameter estimation of 2-D polynomial cubic signals using cubic phase function with genetic algorithms’, Signal Process., 2010, 90, (9), pp. 26982707.
    18. 18)
      • 18. O'Shea, P.: ‘On refining polynomial phase signal parameter estimates’, IEEE Trans. Aerosp. Electron. Syst., 2010, 46, (3), pp. 978987.
    19. 19)
      • 19. Ristić, B., Boashash, B.: ‘Comments on ‘the Cramer-Rao lower bounds for signals with constant amplitude and polynomial phase’’, IEEE Trans. Signal Process., 1998, 46, (6), pp. 17081709.
    20. 20)
      • 20. Gini, F., Giannakis, G.B.: ‘Hybrid FM-polynomial phase signal modeling: parameter estimation and Cramer-Rao bounds’, IEEE Trans. Signal Process., 1999, 47, (2), pp. 363377.
    21. 21)
      • 21. Wang, P., Orlik, P.V., Sadamoto, K., et al: ‘Parameter estimation of hybrid sinusoidal FM-polynomial phase signal’, IEEE Signal Process. Lett., 2017, 24, (1), pp. 6670.
    22. 22)
      • 22. Djurović, I., Stanković, LJ.: ‘Quasi maximum likelihood estimator of polynomial phase signals’, IET Signal Process., 2014, 13, (4), pp. 347359.
    23. 23)
      • 23. Djurović, I.: ‘On parameters of the QML PPS estimator’, Signal Process., 2015, 116, pp. 16.
    24. 24)
      • 24. Djurović, I., Stanković, LJ.: ‘STFT-based estimator of polynomial phase signals’, Signal Process., 2012, 92, (11), pp. 27692774.
    25. 25)
      • 25. Djurović, I.: ‘Quasi ML algorithm for 2-D PPS estimation’, Multidimens. Signals Syst., 2017, 28, (2), pp. 371389.
    26. 26)
      • 26. Djurović, I.: ‘High precision technique for PPS estimation in impulsive noise environment’, Signal Process., 2016, 127, pp. 151155.
    27. 27)
      • 27. Djurović, I.: ‘QML-RANSAC: PPS and FM signals estimation in heavy noise environments’, Signal Process., 2017, 130, pp. 142151.
    28. 28)
      • 28. Djurović, I., Simeunović, M.: ‘Resolving aliasing effect in the QML estimation of PPSs’, IEEE Trans. Aerosp. Electron. Syst., 2016, 52, (3), pp. 14941499.
    29. 29)
      • 29. Djurović, I., Popović-Bugarin, V., Simeunović, M.: ‘The STFT-based estimator of micro-Doppler parameters’, IEEE Trans. Aerosp. Electron. Syst., 2017, 53, (3), pp. 12731283.
    30. 30)
      • 30. Stanković, L.J., Djurović, I., Stanković, S., et al: ‘Instantaneous frequency in time-frequency analysis: enhanced concepts and performance of estimation algorithms’, Digit. Signal Process., 2014, 35, pp. 113.
    31. 31)
      • 31. Lerga, J., Sučić, V.: ‘Nonlinear IF estimation based on the pseudo WVD adapted using the improved sliding pairwise ICI rule’, IEEE Signal Process. Lett., 2009, 16, (11), pp. 953956.
    32. 32)
      • 32. Papoulis, A.: ‘Signal analysis’ (McGraw-Hill, New York, USA, 1997).
    33. 33)
      • 33. Oppenheim, A., Schafer, R.W.: ‘Discrete-time signal processing’ (Prentice-Hall, New Jersey, USA, 2009, 3rd edn.).
    34. 34)
      • 34. Stanković, S., Stanković, L.J., Ivanović, V.N., et al: ‘An architecture for the VLSI design of systems for time-frequency analysis and time-varying filtering’, Ann. Telecommun., 2002, 57, (9/10), pp. 974995.
    35. 35)
      • 35. Liu, K.J.R.: ‘Novel parallel architectures for short-time Fourier transform’, IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process., 1993, 40, (12), pp. 786790.
    36. 36)
      • 36. Amin, M.G.: ‘A new approach to recursive Fourier transform’, Proc. IEEE, 1987, 75, (11), pp. 15371538.
    37. 37)
      • 37. Unser, M.: ‘Recursion in short time signal analysis’, Signal Process., 1983, 5, (5), pp. 229240.
    38. 38)
      • 38. Amin, M.G.: ‘Spectral smoothing and recursion based on the nonstationarity of the autocorrelation function’, IEEE Trans. Signal Process., 1993, 41, (2), pp. 930934.
    39. 39)
      • 39. Amin, M.G., Feng, K.D.: ‘Short time Fourier transform using cascade filter structures’, IEEE Trans. Circuits Syst. II, Analog Digit. Signal Process., 1995, 42, (10), pp. 631641.
    40. 40)
      • 40. Boashash, B.: ‘Time frequency signal analysis and processing: a comprehensive reference’ (Elsevier, Oxford, UK, 2016, 2nd edn.).
    41. 41)
      • 41. Sejdić, E., Djurović, I., Jiang, J.: ‘Time-frequency feature representation using energy concentration: an overview of recent advances’, Digit. Signal Process., 2009, 19, (1), pp. 153183.
    42. 42)
      • 42. Ivanović, V.N., Stojanović, R., Stanković, LJ.: ‘Multiple clock cycle architecture for the VLSI design of a system for time-frequency analysis’, EURASIP J. Appl. Signal Process., Spec. Issue Des. Methods DSP Syst., 2006, 2006, pp. 118.
    43. 43)
      • 43. Ivanović, V.N., Stojanović, R.: ‘An efficient hardware design of the flexible 2-D system for space/spatial-frequency signal analysis’, IEEE Trans. Signal Process., 2007, 55, (6), pp. 31163125.
    44. 44)
      • 44. Ivanović, V.N., Jovanovski, S.: ‘Signal adaptive system for time–frequency analysis’, Electron. Lett., 2008, 44, (21), pp. 12791280.
    45. 45)
      • 45. Ivanović, V.N., Jovanovski, S.: ‘Signal adaptive system for space/spatial–frequency analysis’, EURASIP J. Adv. Signal Process.2009, 2009, pp. 116.
    46. 46)
      • 46. Jovanovski, S., Ivanović, V.N.: ‘Signal adaptive pipelined hardware design of time-varying optimal filter for highly nonstationary FM signal estimation’, J. Signal. Process. Syst., 2011, 62, (3), pp. 287300.
    47. 47)
      • 47. Ivanović, V.N., Jovanovski, S., Radović, N.: ‘Superior execution time design of optimal (Wiener) time-frequency filter’, Electron. Lett., 2016, 52, (17), pp. 14401442.
    48. 48)
      • 48. Ivanović, V.N., Radović, N., Jovanovski, S.: ‘Real–time design of space/spatial–frequency optimal filter’, Electron. Lett., 2010, 46, (25), pp. 16961697.
    49. 49)
      • 49. Ivanović, V.N., Radović, N.: ‘Signal adaptive hardware implementation of a system for highly nonstationary two-dimensional FM signal estimation’, AEUE – Int. J. Electron. Commun., 2015, 69, (12), pp. 18541867.
    50. 50)
      • 50. Ivanović, V.N., Daković, M., Stanković, LJ.: ‘Performances of quadratic time-frequency distributions as instantaneous frequency estimators’, IEEE Trans. Signal Process., 2003, 51, (1), pp. 7789.
    51. 51)
      • 51. Stanković, LJ., Daković, M., Ivanović, V.N.: ‘Performances of spectrogram as an IF estimator’, Electron. Lett., 2001, 37, (12), pp. 797799.
    52. 52)
      • 52. Ivanović, V.N., Daković, M., Djurović, I., et al: ‘Instantaneous frequency estimation by using time-frequency distributions’. Proc. of IEEE Int. Conf. on Acoustics, Speech, and Signal Processing (ICASSP) 2001, Salt Lake City, May 2001, pp. 35213524.
    53. 53)
      • 53. Ivanović, V.N., Stanković, LJ., Petranović, D.: ‘Finite word-length effects in implementation of algorithms for time-frequency signal analysis’, IEEE Trans. Signal Process., 1998, 46, (7), pp. 20352041.
http://iet.metastore.ingenta.com/content/journals/10.1049/iet-cds.2018.5112
Loading

Related content

content/journals/10.1049/iet-cds.2018.5112
pub_keyword,iet_inspecKeyword,pub_concept
6
6
Loading
This is a required field
Please enter a valid email address