Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
A 2-D robust high-resolution frequency estimation approach
Signal Processing
A novel algorithm for two-dimensional frequency estimation
Signal Processing
SVD-based joint azimuth/elevation estimation with automatic pairing
Signal Processing
An efficient approach for two-dimensional parameter estimation of a single-tone
IEEE Transactions on Signal Processing
Two-dimensional modal analysis based on maximum likelihood
IEEE Transactions on Signal Processing
Estimating two-dimensional frequencies by matrix enhancement andmatrix pencil
IEEE Transactions on Signal Processing
Joint angle and delay estimation using shift-invariance techniques
IEEE Transactions on Signal Processing
Estimation of frequencies and damping factors by two-dimensionalESPRIT type methods
IEEE Transactions on Signal Processing
First-Order Perturbation Analysis of Singular Vectors in Singular Value Decomposition
IEEE Transactions on Signal Processing - Part I
Partial forward-backward averaging for enhanced frequency estimation of real X-texture modes
IEEE Transactions on Signal Processing
A generalized weighted linear predictor frequency estimation approach for a complex sinusoid
IEEE Transactions on Signal Processing
Efficient source enumeration for accurate direction-of-arrival estimation in threshold region
Digital Signal Processing
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In this paper, we tackle the two-dimensional (2-D) parameter estimation problem for a sum of K=2 real/complex damped sinusoids in additive white Gaussian noise. According to the rank-property of the 2-D noise-free data matrix, the damping factor and frequency information is contained in the dominant left and right singular vectors. Using the sinusoidal linear prediction property of these vectors, the frequencies and damping factors of the first dimension are first estimated. The parameters of the second dimension are then computed such that frequency pairing is automatically achieved. Computer simulations are included to compare the proposed approach with several conventional 2-D estimators in terms of mean square error performance and computational complexity.