Quantifying predictability for applications in signal separation

Traditional signal processing techniques normally apply stochastic process theory to account for the inability to predict, control, or reproduce precise results in repeated experiments. This often requires fairly restrictive assumptions (e.g., linear and Gaussian) regarding the nature of the processes generating the signal source and its contamination. Our purpose is to provide a preliminary analysis of an alternative model to account for this random behavior. The alternative model assumes that randomness can result from chaotic dynamics in the processes that generate and contaminate the signal of interest. This provides the option to use nonlinear dynamic prediction models instead of traditional statistical modeling for signal separation. The effectiveness of a given prediction model for a particular application can then be interpreted in terms of the predictability of the data set using that model.