A LINEAR INTERPOLATORY ALGORITHM FOR ROBUST SYSTEM-IDENTIFICATION WITH CORRUPTED MEASUREMENT DATA

This note 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. A key idea of this robust linear algorithm is that 1) the approximation is separated into two components: real and imaginary parts and 2) Fejer (Hermite) interpolation. Because of the Fejer interpolation, the data points are required at the Chebyshev points.