Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1109/ICSIPA.2011.6144119 | ICSIPA |
Keywords | Field | DocType |
gaussian mixture noise,cramer-rao bound,signal processing,snr estimator,gaussian noise,nongaussian distribution,signal-to-noise ratio,additive noise,gaussian distribution,maximum likelihood estimate,signal to noise ratio,cramer rao bound,robustness,maximum likelihood estimation | Signal processing,Gaussian random field,Pattern recognition,Computer science,Signal-to-noise ratio,Communication channel,Robustness (computer science),Artificial intelligence,Gaussian noise,Additive white Gaussian noise,Estimator | Conference |
ISBN | Citations | PageRank |
978-1-4577-0243-3 | 3 | 0.42 |
References | Authors | |
2 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Ying Siew Lo | 1 | 4 | 0.77 |
| Heng-Siong Lim | 2 | 45 | 9.65 |
| Alan Wee-Chiat Tan | 3 | 9 | 3.59 |