A Linear Robustly Convergent Interpolatory Algorithm For System Identification

This paper presents a linear, robustly convergent interpolatory algorithm for system identification in the presence of bounded noise. The proposed algorithm converges to the actual, but unknown system in frequency domain in the noise free case and maintains the robust convergence result in the face of bounded noise. This robustness property distinguishes the proposed linear algorithm from other existing linear schemes.