Likelihood Ratios for Sequential Decision-Making on Markov Sequences.
COLORADO STATE UNIV FORT COLLINS DEPT OF ELECTRICAL ENGINEERING
Pagination or Media Count:
The purpose of this research is to derive a variety of likelihood ratios for the detection of Markov sequences in noise. The program consists of exploiting the Bayesian recursions appropriate to a related filtering problem, together with a known-form likelihood ratio, to obtain the desired result. In the derivation of a discrete-time Gauss-Markov likelihood ratio the authors seek a pure causal estimator-correlator structure and encounter a locally stable state estimator that is of some interest in its own right. The likelihood ratio is pure in the sense that the locally stable estimator is used in precisely the same manner as the stored replica in a known-form detection problem. The estimator is locally stable in the sense that it equalizes within a constant related to the a priori and a posteriori filtering error covariances, the prior and a posteriori filtering densities.
- Non-Radio Communications