System identification
Fundamentals of statistical signal processing: estimation theory
Fundamentals of statistical signal processing: estimation theory
An efficient algorithm for two-dimensional frequency estimation
Multidimensional Systems and Signal Processing
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing
Reformulation of Pisarenko harmonic decomposition method for single-tone frequency estimation
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
Multidimensional Frequency Estimation With Finite Snapshots in the Presence of Identical Frequencies
IEEE Transactions on Signal Processing
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
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In this paper, parameter estimation of a two-dimensional (2-D) single damped real/complex tone in the presence of additive white Gaussian noise is addressed. By utilizing the rank-one property of the 2-D noise-free data matrix, the damping factor and frequency for each dimension are estimated in a separable manner from the principal left and right singular vectors according to an iterative weighted least squares procedure. The remaining parameters are then obtained straightforwardly using standard least squares. The biases as well as variances of the damping factor and frequency estimates are also derived, which show that they are approximately unbiased and their performance achieves Cramér-Rao lower bound (CRLB) at sufficiently large signal-to-noise ratio (SNR) and/or data size conditions. We refer the proposed approach to as principal-singular-vector utilization for modal analysis (PUMA) which performs estimation in a fast and accurate manner. The development and analysis can easily be adapted for a tone which is undamped in at least one dimension. Furthermore, comparative simulation results with several conventional 2-D estimators and CRLB are included to corroborate the theoretical development of the PUMA approach as well as to demonstrate its superiority.