A combined stochastic/decision-aided SNR estimator
A problem of a blind stochastic SNR estimator and a principle of SNR estimation combining a blind estimator and decision-aided estimator are presented. Signal and noise can be detected from the received signal by a decision depending on a decision threshold that is made available by using the second and fourth-order moments of the received signal. Subsequently, the SNR is computed from the detected signal and the detected noise. The bias of SNR estimation (based on detection errors) is removed by applying analytically bias correction.
An SNR estimation algorithm using fourth-order moments
An algorithm is presented that allows an estimation of the SNR just by the observation of the noisy signal. For the estimation, shape factors of the signal's and the noise's probability density functions are used. The algorithm is based on the second and fourth order moments of the observed noisy signal.<<ETX>>
Velocity moments in alongshore bottom stress parameterizations
The time-averaged alongshore bottom stress is an important component of nearshore circulation models. In a widely accepted formulation the bottom stress is proportional to , the time average of the product of the instantaneous velocity magnitude |u→| and the instantaneous alongshore velocity component υ. Both mean and fluctuating (owing to random, directionally spread waves) velocities contribute to . Direct estimation of requires a more detailed specification of the velocity field than is usually available, so the term is parameterized. Here direct estimates of based on time series of near-bottom currents observed between the shoreline and 8-m water depth are used to test the accuracy of parameterizations. Common parameterizations that are linear in the mean alongshore current significantly underestimate for moderately strong alongshore currents, resulting in overestimation of a drag coefficient determined by fitting modeled (with a linearized bottom stress) to observed alongshore currents. A parameterization based on a joint-Gaussian velocity field with the observed velocity statistics gives excellent overall agreement with the directly estimated and allows analytic investigation of the statistical properties of the velocity field that govern . Except for the weakest flows, depends strongly on the mean alongshore current and the total velocity variance but depends only weakly on the mean wave angle, wave directional spread, and mean cross-shore current. Several other nonlinear parameterizations of are shown to be more accurate than the linear parameterizations and are adequate for many modeling purposes.