Knowledge-based approach to signal smoothing

The analytic approach to signal processing performs well if there is adequate understanding of the characteristics of the signal source. In more complicated cases, syntactic signal processing tools used to be a working alternative; however, these share the common algorithmic background with the numerical methods. On the other hand, the filed area of order statistics (OS) introduced into signal processing a number of tools that handle phenomena that the usual analytic theory could not even model. To grasp the essence of the filtering operation requires a kind of symbolical description, ambiguous and full of dependencies, creating a gap between the filed and other customary areas of signal processing. Thus, proper choice of an OS filter for a given application must be based on a mixed numerical versus symbolical evaluation of the signal features and goals, which is clearly outside the scope of normal signal-processing expertise. A possible solution to this problem is to interface the OS tool library to the user via an advisory layer capable of the integrated maintenance of the quantitative and symbolic information, supporting the user in the modelling, decision and evaluation phases of problem-solving. The study presented in this paper addresses the concrete case of OS signal smoothing, evaluating the components of the problem and presenting the structure of the intelligent front-end system