论文信息 - Performance comparison of SNR estimators in Gaussian mixture noise

Performance comparison of SNR estimators in Gaussian mixture noise

Most of the signal-to-noise ratio (SNR) estimators published in literature are designed based on Gaussian noise assumption. These estimation schemes typically perform poorly when the additive noise has a non-Gaussian distribution. This paper investigates the robustness of several popular SNR estimators in two-term Gaussian mixture noise. The Cramer-Rao bound is derived and used as a benchmark against which the performance of the estimators is measured. Simulations results show that the SNR estimators suffer performance degradation in non-Gaussian noise channels.

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