On the uniqueness of the inverse logarithmic potential problem
SIAM Journal on Applied Mathematics
Matrix computations (3rd ed.)
Structured total least squares problems: formulations, algorithms and applications
Proceedings of the second international workshop on Recent advances in total least squares techniques and errors-in-variables modeling
A Stable Numerical Method for Inverting Shape from Moments
SIAM Journal on Scientific Computing
The Generalized Eigenvalue Problem for Nonsquare Pencils Using a Minimal Perturbation Approach
SIAM Journal on Matrix Analysis and Applications
A parameter estimation scheme for damped sinusoidal signals basedon low-rank Hankel approximation
IEEE Transactions on Signal Processing
Reconstructing polygons from moments with connections to arrayprocessing
IEEE Transactions on Signal Processing
The constrained total least squares technique and its applicationsto harmonic superresolution
IEEE Transactions on Signal Processing
Shape from moments - an estimation theory perspective
IEEE Transactions on Signal Processing
URV ESPRIT for tracking time-varying signals
IEEE Transactions on Signal Processing
Formulation and solution of structured total least norm problemsfor parameter estimation
IEEE Transactions on Signal Processing
Overview of total least-squares methods
Signal Processing
Strongly concave star-shaped contour characterization by algebra tools
Signal Processing
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In this paper we discuss the problem of recovering the vertices of a planar polygon from its measured complex moments. Because the given, measured moments can be noisy, the recovered vertices are only estimates of the true ones. The literature offers many algorithms for solving such an estimation problem. We will restrict our discussion to the Total Least Squares (TLS) data fitting models HTLS and STLS and the matrix pencil method GPOF. We show the close link between the HTLS and the GPOF method. We use the HTLS method to compute starting values for the STLS method. We compare the accuracy of these three methods on simulated data.