An Efficient Parametric Model-based Framework for Recursive Frequency/Spectrum Estimation of Nonstationary Signal

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    The manuscript intends to a design a general form of computationally efficient parametric mechanism based model to estimate the recursive frequency/spectrum and describe the nonlinear signals which consists of diverse degrees of nonlinearity and and indiscreet units. The time variant frequency estimation is defined as the as a time-varying model recognizable proof issue in which faulty/failure data are evaluated by model coefficients. In this, anestimation approach of QR-disintegration based recursive slightest M-gauge (QRRLM) is utilized for estimation of recursive time-vareint model coefficients in non-linear environment conditionby utilizing M-estimation. Here, a Veriable Forgetting Factor Control (VFFC) are designed to enhance the exection of QRRLM mechanism in nonlinear condition. In this, a hypothetical deduction and re-enactments approaches were used which helps to perform VFFC determination. The resultant VFFC-QRRLM estimation can confine and limit the faulty unitswhile dealing with different degrees of nonlinearvariations. Recreation comes about demonstrate that the proposed VFF-QRRLM calculation is more vigorous and exact than traditional recursive minimum squares-based techniques in evaluating both time-shifting narrowband recurrence segments and broadband otherworldly segments with incautious parts. Potential uses of the proposed technique can be found in quality force checking, online deficiency location, and discourse examination.

     


  • Keywords


    Spectral Estimation, M-Estimation, RecursiveFrequency Estimation, Time-Varying Linear Model, Variable Forgetting Factor.

  • References


      H. Chen, Z. Wang, Y. Lu, D. Li and T. Li, "Cumulant-Based RLS Algorithm with Variable Forgetting Factor to Estimate Time-Varying Interharmonics," Instrumentation and Measurement, Computer, Communication and Control (IMCCC), 2014 Fourth International Conference on, Harbin, 2014, pp. 351-356.

      [2] Z. G. Zhang, S. C. Chan and X. Chen, "A new Kalman filter-based recursive method for measuring and tracking time-varying spectrum of nonstationary signals," Information, Communications and Signal Processing (ICICS) 2013 9th International Conference on, Tainan, 2013, pp. 1-4.

      [3] Z. G. Zhang and S. C. Chan, "Recursive Parametric Frequency/Spectrum Estimation for Nonstationary Signals With Impulsive Components Using Variable Forgetting Factor," in IEEE Transactions on Instrumentation and Measurement, vol. 62, no. 12, pp. 3251-3264, Dec. 2013.

      [4] Z. Xiaoming and Z. Zhongzhao, "Parameter estimation of DSSS signals in non-cooperative communication system," in Journal of Systems Engineering and Electronics, vol. 18, no. 1, pp. 14-21, March 2007.

      [5] S. Y. Jeong, K. Kim, J. H. Jeong, K. C. Oh and J. Kim, "Adaptive noise power spectrum estimation for compact dual channel speech enhancement," 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, 2010, pp. 1630-1633.

      [6] J. S. Erkelens and R. Heusdens, "Tracking of Nonstationary Noise Based on Data-Driven Recursive Noise Power Estimation," in IEEE Transactions on Audio, Speech, and Language Processing, vol. 16, no. 6, pp. 1112-1123, Aug. 2008.

      [7] X. m. Zhang and Z. z. Zhang, "Parameter Estimation of DSSS Signals with Line Enhancement," 2006 IEEE International Conference on Information Acquisition, Shandong, 2006, pp. 127-132.

      [8] M. P. Tarvainen, S. Georgiadis and P. A. Karjalainen, "Time-Varying Analysis of Heart Rate Variability with Kalman Smoother Algorithm," 2005 IEEE Engineering in Medicine and Biology 27th Annual Conference, Shanghai, 2005, pp. 2718-2721.

      [9] H. Khalilinia; V. Venkatasubramanian, "Recursive Frequency Domain Decomposition for Multidimensional Ambient Modal Estimation," in IEEE Transactions on Power Systems , vol.PP, no.99, pp.1-1.

      [10] J. M. Bruno and B. L. Mark, "A recursive algorithm for joint time-frequency wideband spectrum sensing," Wireless Communications and Networking Conference Workshops (WCNCW), 2015 IEEE, New Orleans, LA, 2015, pp. 235-240.

      [11] G. O. Glentis, "Efficient Algorithms for Adaptive Capon and APES Spectral Estimation," in IEEE Transactions on Signal Processing, vol. 58, no. 1, pp. 84-96, Jan. 2010.

      [12] S. Y. Jeong, K. Kim, J. H. Jeong, K. C. Oh and J. Kim, "Adaptive noise power spectrum estimation for compact dual channel speech enhancement," 2010 IEEE International Conference on Acoustics, Speech and Signal Processing, Dallas, TX, 2010, pp. 1630-1633.


 

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Article ID: 20227
 
DOI: 10.14419/ijet.v7i4.6.20227




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