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High-Resolution Spectrum Estimation

High-Resolution Spectrum Estimation

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In this chapter, some spectrum estimation approaches have been introduced that has been referred to as high-resolution spectrum estimations. Their major advantage is that they can provide higher frequency resolution than FFT, especially on simultaneous signals. In this chapter, seven high-resolution methods have been discussed: linear predication (or autoregressive (AR)) method, Prony's method, the least squares Prony's method, the multiple signal classification (MUSIC) method, the estimation of signal parameters via rotational invariance techniques (ESPRIT) method, the minimum norm method, and the minimum norm with discrete Fourier transform (DFT) method.

Chapter Contents:

  • 14.1 Introduction
  • 14.2 Autoregressive (AR) Method
  • 14.3 Yule-Walker Equation
  • 14.4 Levinson-Durbin Recursive Algorithm
  • 14.5 Input Data Manipulations
  • 14.5.1 Covariance Method
  • 14.5.2 Autocorrelation Method
  • 14.6 Backward Prediction and Modified Covariance Method
  • 14.7 Burg Method
  • 14.8 Order Selection
  • 14.9 Prony's Method
  • 14.10 Prony's Method Using the Least Squares Approach
  • 14.11 Eigenvectors and Eigenvalues
  • 14.12 MUSIC Method
  • 14.13 ESPRIT Method
  • 14.14 Minimum Norm Method
  • 14.15 Minimum Norm Method With Discrete Fourier Transform
  • 14.16 Adaptive Spectrum Estimation
  • References
  • Appendix 14.A
  • Appendix 14.B
  • Appendix 14.C
  • Appendix 14.D
  • Appendix 14.E
  • Appendix 14.F
  • Appendix 14.G
  • Appendix 14.H
  • Appendix 14.I

Inspec keywords: signal classification; least squares approximations; signal resolution; discrete Fourier transforms

Other keywords: spectrum estimation approaches; discrete Fourier transform method; ESPRIT method; least squares Prony's method; multiple signal classification method; higher frequency resolution; linear predication method; high resolution spectrum estimation; MUSIC method; minimum norm method; DFT method; estimation of signal parameters via rotational invariance techniques

Subjects: Digital signal processing; Integral transforms in numerical analysis; Signal processing and detection; Interpolation and function approximation (numerical analysis); Interpolation and function approximation (numerical analysis); Integral transforms in numerical analysis

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