NONLINEAR IDENTIFICATION USING COMPRESSED SENSING AND MINIMAL SYSTEM SAMPLING

摘要

Compressed sensing is used to determine a model of a nonlinear system. In one example, L1-norm minimization is used to fit a generic model function to a set of samples thereby obtaining a fitted model. Convex optimization can be used to determine model coefficients that minimize the L1-norm. In one application, the fitted model is used to calibrate a predistorter. In another application, the fitted model function is used to predict future actions of the system. The generic model is made of up of constituent functions that may or may not be orthogonal to one another. In one example, an initial model function of non-orthogonal constituent functions is orthogonalized to generate a generic model function of constituent orthogonal functions. Although the number of samples to which the generic model is fitted can be less than the number of model coefficients, the fitted model nevertheless accurately models system nonlinearities.