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A quadratic programming-based method for quantized system identification

This paper proposes a quadratic programming (QP)-based method, for linear dynamic system identification from quantized data or binary measurements. The main idea of the proposed method is to reformulate the identification problem for finite impulse (FIR) systems, usually viewed as a nonlinear estimation problem with discontinuous nonlinearities, in the form of a standard QP problem, which is a convex optimization problem and can be solved efficiently. The QP-based method is equally applicable to both quantized data and binary measurements without any modification. It has no special assumptions on the identification experiments. An iterative QP-based method is also developed for the identification of infinite impulse response (IIR) systems. Numerical examples demonstrate the effectiveness of the proposed method.