Title :
Performance analysis of parameter estimation of superimposed signals by dynamic programming
Author :
Yau, Sze Fong ; Bresler, Yoram
Author_Institution :
Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL, USA
Abstract :
The problem of fitting a model composed of a number of superimposed signals to noisy data using the maximum likelihood (ML) criterion is considered. A dynamic programming (DP) algorithm which solves the problem efficiently is presented. An asymptotic property of the estimates is derived, and a bound on the bias of the estimates is given. The bound is then computed using perturbation analysis and compared with computer simulation results. The results show that the DP algorithm is a versatile and efficient algorithm for parameter estimation. In practical applications, the estimates can be refined by a local search (e.g., the Gauss-Newton method) of the exact ML criterion, initialized by the DP estimates
Keywords :
dynamic programming; parameter estimation; perturbation techniques; signal processing; Gauss-Newton method; asymptotic property; dynamic programming; local search; maximum likelihood criterion; noisy data; parameter estimation; perturbation analysis; signal processing; superimposed signals; Application software; Computer simulation; Dynamic programming; Heuristic algorithms; Least squares methods; Maximum likelihood estimation; Newton method; Parameter estimation; Performance analysis; Recursive estimation;
Conference_Titel :
Acoustics, Speech, and Signal Processing, 1991. ICASSP-91., 1991 International Conference on
Conference_Location :
Toronto, Ont.
Print_ISBN :
0-7803-0003-3
DOI :
10.1109/ICASSP.1991.150126