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

Authors

  • Kantipudi MVV Prasad
  • Dr. H.N. Suresh
  • Rajanikanth Aluvalu

DOI:

https://doi.org/10.14419/ijet.v7i4.6.20227

Published:

2018-09-25

Keywords:

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

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.

 

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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