By Topic

Discrete optimal linear smoothing for systems with uncertain observations

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
$31 $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

1 Author(s)

The smoothing filter and smoothing error covariance matrix equations are developed for discrete linear systems whose observations may contain noise alone, where only the probability of occurrence of such cases is known to the estimator. An example of such a system arises in trajectory tracking, where the signal is first detected and then is processed by the estimator for tracking purposes. The results apply to any detection decision process, however, any such decision is associated with a false alarm probability, which is the probability that the detected signal contains only noise. The present results together with the earlier work of Nahi on prediction and filtering give a complete treatment of the discrete linear estimation problem for systems characterized by uncertain observations. These results, of course, reduce to well-known formulations for the classical estimation problem in the case where the observation is always assumed to contain the signal to be estimated.

Published in:

Information Theory, IEEE Transactions on  (Volume:21 ,  Issue: 3 )