Matrix analysis
Multiplicative noise models: parameter estimation using cumulants
Signal Processing - Special issue on higher order statistics
Wavelet packets-based high-resolution spectral estimation
Signal Processing
Harmonic retrieval in colored non-Gaussian noise using cumulants
IEEE Transactions on Signal Processing
Cumulant-based approach to harmonic retrieval and related problems
IEEE Transactions on Signal Processing
Estimating two-dimensional frequencies by matrix enhancement andmatrix pencil
IEEE Transactions on Signal Processing
Harmonic retrieval via state space and fourth-order cumulants
IEEE Transactions on Signal Processing
Harmonics in multiplicative and additive noise: parameterestimation using cyclic statistics
IEEE Transactions on Signal Processing
SVD analysis by synthesis of harmonic signals
IEEE Transactions on Signal Processing
Harmonic retrieval using higher order statistics: a deterministicformulation
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Paper: Modeling by shortest data description
Automatica (Journal of IFAC)
A hybrid approach to harmonic retrieval in non-Gaussian ARMA noise
IEEE Transactions on Information Theory
Two-dimensional harmonic retrieval in correlative noise based on genetic algorithm
EURASIP Journal on Advances in Signal Processing - Special issue on robust processing of nonstationary signals
Detecting the number of 2-D harmonics in multiplicative and additive noise using enhanced matrix
Digital Signal Processing
Harmonic retrieval by period blind source extraction method: Model and algorithm
Digital Signal Processing
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This paper presents a method for estimating the number of harmonics in multiplicative and additive noise using enhanced matrix. We construct an enhanced matrix from the data samples, and then analyze the eigenvalues of the covariance matrix of the enhanced matrix. The number of harmonics in multiplicative and additive noise is inherent with the eigenvalues and it can be estimated using the special property of the eigenvalues. The proposed method avoids the peaks searching and does not assume the distribution and color of the multiplicative and additive noise. Simulation results are presented to demonstrate the effectiveness of the proposed algorithm.