Title :
Iterative solutions of min-max parameter estimation with bounded data uncertainties
Author :
Sayed, Ali H. ; Garulli, Andrea ; Chandrasekaran, S.
Author_Institution :
Dept. of Electr. Eng., California Univ., Los Angeles, CA, USA
Abstract :
This paper deals with the important problem of parameter estimation in the presence of bounded data uncertainties. Its recent closed-form solution leads to more meaningful results than alternative methods (e.g., total least-squares and robust estimation), when a priori bounds about the uncertainties are available. The derivation requires the computation of the SVD of the data matrix and the determination of the unique positive root of a nonlinear equation. This paper establishes the existence of a fundamental contraction mapping and uses this observation to propose an approximate recursive algorithm that avoids the need for explicit SVDs and for the solution of the nonlinear equation. Simulation results are included to demonstrate the good performance of the recursive scheme
Keywords :
iterative methods; minimax techniques; nonlinear equations; parameter estimation; recursive estimation; singular value decomposition; SVD; a priori bounds; approximate recursive algorithm; bounded data uncertainties; closed-form solution; data matrix; fundamental contraction mapping; iterative solutions; min-max parameter estimation; nonlinear equation; unique positive root; Closed-form solution; Cost function; Noise robustness; Nonlinear equations; Parameter estimation; Recursive estimation; Resonance light scattering; Uncertainty;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1997. ICASSP-97., 1997 IEEE International Conference on
Conference_Location :
Munich
Print_ISBN :
0-8186-7919-0
DOI :
10.1109/ICASSP.1997.604635