Nonlinear noise reduction through Monte Carlo sampling.
We consider the problem of nonlinear noise reduction within the framework of Bayesian Theory. This enables us to place appropriate weights on the measurement and dynamic errors and thereby avoid over cleaning the data. Using a Metropolis-Hastings sampler, we are able to achieve robust noise reduction without the introduction of ad hoc parameters but at the expense of higher computational complexity. Such an algorithm should also allow us to explore the potential and limitations of other noise reduction methods. (c) 1998 American Institute of Physics.
Davies
M. E.
ME
Signal Processing and Communications Group, Cambridge University Engineering Department, Trumpington Street, Cambridge CB2 1PZ, United Kingdom.