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arXiv · 1108.3410

Minimum Mean Square Error Estimation Under Gaussian Mixture Statistics

John T. Flam·Saikat Chatterjee·Kimmo Kansanen·Torbjorn Ekman
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Abstract

This paper investigates the minimum mean square error (MMSE) estimation of x, given the observation y = Hx+n, when x and n are independent and Gaussian Mixture (GM) distributed. The introduction of GM distributions, represents a generalization of the more familiar and simpler Gaussian signal and Gaussian noise instance. We present the necessary theoretical foundation and derive the MMSE estimator for x in a closed form. Furthermore, we provide upper and lower bounds for its mean square error (MSE). These bounds are validated through Monte Carlo simulations.

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John T. Flam, Saikat Chatterjee, Kimmo Kansanen, Torbjorn Ekman. 2011-08-17. Minimum Mean Square Error Estimation Under Gaussian Mixture Statistics. https://arxiv.org/abs/1108.3410

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