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Noise space decomposition method for two-dimensional sinusoidal model

Author

Listed:
  • Nandi, Swagata
  • Kundu, Debasis
  • Srivastava, Rajesh Kumar

Abstract

The estimation of the parameters of the two-dimensional sinusoidal signal model has been addressed. The proposed method is the two-dimensional extension of the one-dimensional noise space decomposition method. It provides consistent estimators of the unknown parameters and they are non-iterative in nature. Two pairing algorithms, which help in identifying the frequency pairs have been proposed. It is observed that the mean squared errors of the proposed estimators are quite close to the asymptotic variance of the least squares estimators. For illustrative purposes two data sets have been analyzed, and it is observed that the proposed model and the method work quite well for analyzing real symmetric textures.

Suggested Citation

  • Nandi, Swagata & Kundu, Debasis & Srivastava, Rajesh Kumar, 2013. "Noise space decomposition method for two-dimensional sinusoidal model," Computational Statistics & Data Analysis, Elsevier, vol. 58(C), pages 147-161.
    • Handle: RePEc:eee:csdana:v:58:y:2013:i:c:p:147-161
    DOI: 10.1016/j.csda.2011.03.002
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    References listed on IDEAS

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    1. Bansal, Naveen K. & Hamedani, G. G. & Zhang, Hao, 1999. "Non-linear regression with multidimensional indices," Statistics & Probability Letters, Elsevier, vol. 45(2), pages 175-186, November.
    2. Kundu, Debasis, 1994. "Estimating the parameters of complex-valued exponential signals," Computational Statistics & Data Analysis, Elsevier, vol. 18(5), pages 525-534, December.
    3. Mitra, Amit & Kundu, Debasis & Agrawal, Gunjan, 2006. "Frequency estimation of undamped exponential signals using genetic algorithms," Computational Statistics & Data Analysis, Elsevier, vol. 51(3), pages 1965-1985, December.
    4. Hao Zhang & V. Mandrekar, 2001. "Estimation of Hidden Frequencies for 2D Stationary Processes," Journal of Time Series Analysis, Wiley Blackwell, vol. 22(5), pages 613-629, September.
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