Conceptual diagnosis of signal processing systems

Signal processing systems usually have parameters whose settings are selected on the basis of the class of expected input signals. Finding the appropriate parameter settings for a class of inputs usually involves testing the system against typical and/or important inputs from that class. Whenever the system produces an incorrect output, the parameter settings responsible for the fault are identified. The system user can then adjust the system parameters in order to ensure correct system behavior. The diagnostic process of identifying the parameters responsible for system faults is generally difficult because the signal processing system carries out a complicated mathematical transformation involving a multi-stage algorithm that generates an enormous amount of intermediate data. We develop a new approach to the diagnosis of such systems. The approach is based on the availability of an abstract and possibly qualitative description of the input scenario and the use of an alternative system model derived from the underlying mathematical theory that explicitly represents the phenomena responsible for any incorrect processing.