A Fast Convergence Algorithm for L2-Sensitivity Minimization of 2-D Separable-Denominator State-Space Digital Filters

This paper proposes a fast convergence algorithm for L2-sensitivity minimization problem of two-dimensional (2-D) separable-denominator state-space digital filters subject to L2-scaling constraints. The proposed algorithm reduces a constrained optimization problem to an unconstrained optimization problem by appropriate variable transformation, and minimizes the L2-sensitivity by iterative calculation called successive substitution method. Our novel algorithm can achieve the L2-sensitivity minimization with quite fast convergence behavior.