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. >
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
