Sparse iterative covariance-based estimation approach for processing atmospheric radar data

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    The Doppler estimation is an important problem for Mesosphere–Stratosphere–Troposphere (MST) Radar data for detection and estimation of the weather parameters like turbulence intensity, mean radial velocity, humidity, temperature, wind speed. For Doppler estimation, one has to compute the Power Spectral Density (PSD). Various parametric and nonparametric methods have been developed. Recently, a new category of spectrum estimation method called Sparse Iterative Covariance Based Estimation (SPICE) is also developed. SPICE is a robust, user parameter-free, high resolution, iterative and globally convergent estimation algorithm. In this paper, the simple gradient approach is used for minimization of the weighted covariance estimation analyzing the data collected from the Indian MST radar at Gadanki (13.5°N, 79.2°E). The same method is applied for radar data to estimate the power spectrum and Doppler frequency. The zonal (U), meridional (V), wind speed (W) are calculated and the results have been validated using simultaneous Global Positioning System (GPS) Sonde data.


  • Keywords


    MST Radar; Doppler Profile; Power Spectral Density; SPICE; GPS Sonde.

  • References


      [1] V.K. Anandan V.K. Anandan, Atmospheric Data Processor—Technical and User Reference Manual, NMRF, DOS Publication, Gadanki, India, 2002.

      [2] V. Anandan, P. Balamuralidhar, P. Rao, and A. Jain, “A method for adaptive moments estimation technique applied to MST radar echoes,” in Proc. Prog. Electromagn. Res. Symposium, 1996, pp. 360–365.

      [3] V. Anandan, C. Pan, T. Rajalakshmi, and G. R. Reddy, “Multitaper spectral analysis of atmospheric radar signals,” Annual Geophysics, vol. 22, no. 11, pp. 3995–4003, 2004. https://doi.org/10.5194/angeo-22-3995-2004.

      [4] V. Anandan, G. R. Reddy, and P. Rao, “Spectral analysis of atmospheric radar signal using higher order spectral estimation technique,” IEEE Transactions on Geoscience and Remote sensing, vol. 39, no. 9, pp. 1890–1895, Sep. 2001. https://doi.org/10.1109/36.951079.

      [5] D. U. M. Rao, T. S. Reddy, and G. R. Reddy, “Atmospheric radar signal processing using principal component analysis,” Digit. Signal Process, vol. 32, pp. 79–84, Sep. 2014. https://doi.org/10.1016/j.dsp.2014.05.009.

      [6] Neetha I. Eappen, T. Sreenivasulu Reddy, and G. Ramachandra Reddy, “Semiparametric algorithm for processing MST Radar data,” IEEE Transactions on Geoscience and Remote Sensing, Vol: 54, No. 5, pp. 2713-2721, May 2016.

      [7] P. Stoica, P. Babu, J. Li, “SPICE: a sparse covariance-based estimation method for array Processing”, IEEE Transactions on Signal Processing, 59(2) (2011) 629–638. https://doi.org/10.1109/TSP.2010.2090525.

      [8] P. Stoica, P. Babu, and J. Li, “New method of sparse parameter estimation in separable models and its use for spectral analysis of irregularly sampled data,” IEEE Transactions on Signal Processing, vol. 59, no. 1, pp. 35–47, Jan. 2011. https://doi.org/10.1109/TSP.2010.2086452.

      [9] P. Stoica, P. Babu, “SPICE and LIKES: two hyperparameter-free methods for sparse parameter estimation”, Signal Processing, 92(7) (2012) 1580–1590. https://doi.org/10.1016/j.sigpro.2011.11.010.

      [10] P. H. Hildebrand and R. Sekhon, “Objective determination of the noise level in Doppler spectra,” Journal of Applied Meteorology, vol. 13, no. 7, pp. 808–811, Oct. 1974. https://doi.org/10.1175/1520-0450(1974)013<0808:ODOTNL>2.0.CO;2.

      [11] PetreStoica, DaveZachariah, JianLi, “Weighted SPICE: A unifying approach for hyperparameter-free sparse estimation”, Digital Signal Processing, pp.1-12, 2014.

      [12] T. S. Reddy and G. R. Reddy, “Spectral analysis of atmospheric radar signal using filter banks polyphase approach,” Digital Signal Processing, vol. 20, no. 4, pp. 1061– 1071, Jul. 2010. https://doi.org/10.1016/j.dsp.2009.10.032.

      [13] S. Thatiparthi, R. Gudheti, and V. Sourirajan, “MST radar signal processing using wavelet-based denoising,” IEEE Geoscience and Remote Sensing Letters, vol. 6, no. 4, pp. 752–756, Oct. 2009. https://doi.org/10.1109/LGRS.2009.2024556.

      [14] T. Reddy and G. R. Reddy, “MST radar signal processing using Cepstral thresholding,” IEEE Transactions on Geoscience and Remote Sensing, vol. 48, no. 6, pp. 2704–2710, Jun. 2010. https://doi.org/10.1109/TGRS.2009.2039937.

      [15] P. Stoica and R. L. Moses, Spectral Analysis of Signals, Upper Saddle River, NJ: Prentice-Hall, 2005.


 

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Article ID: 9215
 
DOI: 10.14419/ijet.v7i1.9215




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