Signal representation: An efficient procedure

The two concepts of orthogonal transformations and classical modeling are combined to form an effective procedure for efficiently representing (approximating) signals contained in a general Hilbert space. The signal to be represented is first operated upon by an appropriately chosen orthogonal transformation with the objective of generating a "simpler" transformed signal. This transformed signal is next approximated by the response of a fixed structured dynamic model to a given input. One selects the parameters which govern this model so that its response best matches the transformed signal. This general procedure is then illustrated by the specific task of representing an arbitrary sequence as the unit-impulse response of a linear recursive operator. The attractiveness of linear recursive modeling is demonstrated by the approximation of a sampled electrocardiogram signal.