Identification of State-Space Models With Banded Toeplitz System Matrices

The identification of structured state-space models with multi-diagonal block Teoplitz system matrices is studied in this paper. Due to the non-convex nature of the identification problem, it is difficult to obtain a global optimal solution. To deal with this problem, the concerned state-space model is recasted as an equivalent one with block circulant system matrices and a low-dimension unknown input related term. Then, the identification problem is formulated as a low rank regularized optimization problem which is solved by the sequentially convex programming method. The effectiveness of the proposed identification method is finally verified through numerical simulations.