Robust estimation of the acoustic attenuation parameter

Because of the effect even additive Gaussian noise at the input has on the actual estimation of the attenuation parameter, Q, least squares estimation techniques make poor Q estimators. The errors in Q estimation may have a near Cauchy distribution. Maximum likelihood (ML) estimators based on Laplacian and Cauchy noise are derived and tested on various models. Simpler estimators, such as the median and alpha-trimmed mean (ATM) are also tested on the models. On very realistic models with noise, and on real data, the ML methods fail and the best methods appear to be the median and ATM.