By Topic

Maximum likelihood parameter estimation of superimposed signals by dynamic programming

Sign In

Cookies must be enabled to login.After enabling cookies , please use refresh or reload or ctrl+f5 on the browser for the login options.

Formats Non-Member Member
$33 $13
Learn how you can qualify for the best price for this item!
Become an IEEE Member or Subscribe to
IEEE Xplore for exclusive pricing!
close button

puzzle piece

IEEE membership options for an individual and IEEE Xplore subscriptions for an organization offer the most affordable access to essential journal articles, conference papers, standards, eBooks, and eLearning courses.

Learn more about:

IEEE membership

IEEE Xplore subscriptions

2 Author(s)
S. F. Yau ; Dept. of Electr. & Comput. Eng., Illinois Univ., Urbana, IL, USA ; Y. Bresler

The problem of fitting a model composed of a number of superimposed signals to noisy data using the maximum likelihood criterion is considered. It is shown, using the Cramer-Rao bound for the estimation accuracy, that in many instances, useful models for the composite signal can be restricted without loss of generality to component signals that directly interact only with one or two of their closest neighbors in parameter space. It is shown that for such models, the global extremum of the criterion can be found efficiently by dynamic programming. The computation requirements are linear in the number of signals, rather than exponential as in the case of exhaustive search. The technique applies for arbitrary sampling of the signals. The dynamic programming method is easily adapted to determining the number of signals as well, as is demonstrated using the minimum description length principle. Computer simulation results are given for several examples

Published in:

IEEE Transactions on Signal Processing  (Volume:41 ,  Issue: 2 )