Marc Tomczak

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We present a study of mode variance statistics for three SVD-based estimation methods in the case of a single-mode damped exponential. The methods considered are namely Kumaresan-Tufts, matrix pencil and Kung's direct data approximation. Through first-order perturbation analysis, we derive closed-form expressions of the variance of the complex mode,(More)
We propose an adaptive subband decomposition scheme designed to estimate the parameters of two-dimensional (2D) exponential signals from large data sets. The principle of the method consists to perform recursive 2D decimation and estimation steps. At each resulting subband, a stopping rule is evaluated to decide whether the decomposition should be continued(More)
This article presents a statistical analysis of the Matrix Pencil method for estimating the mode and the amplitude of a single damped complex exponential. This study is based on a perturbation analysis of the mode and the amplitude, assuming a high signal-to-noise ratio. Closed-form expressions of the mean and variance of these perturbations are derived. It(More)
Several methods have been developed for estimating the parameters of damped and undamped exponentials in noise, but the performances of such techniques are generally known only in the undamped case. In this paper, we consider two estimation methods: the Kumaresan–Tufts method and the Matrix Pencil approach, and we obtain their estimation performances in the(More)
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