An improved method of scaling for real-time signal processing applications

Implementing digital signal processing algorithms using fixed-point arithmetic is a difficult task, involving trade-offs to balance the efficiency and noise performance of a given realization. One of the most important components of such design is to minimize the noise generated by quantization and over flow effects. This is generally accomplished by the scaling of signals and coefficients in the fixed-point realization based upon knowledge of signal features and statistics. This paper presents a new method for structuring this design task. It uses a model based on scaled-fractional numbers to simplify both the concepts required to realize fixed-point arithmetic versions of algorithms, as well as the real effort required for such implementations. This model has been implemented as a data type in a high level computer language to allow direct implementation of fixed-point arithmetic versions of algorithms.<
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