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Linear Filtering and State Space Representations of Hidden Markov Models

In English

Författare

Summary, in English

The topic of this paper is linear filtering of hidden Markov models (HMMs) and linear innovation form representations of HMMs. The possibility to represent the widely used HMM as a state space model is interesting in its own respect, but our interest also comes from subspace estimation methods. To be able to fit the HMM into the framework of subspace methods the process needs to be formulated in state space form. This reformulation is complicated by the non-minimality within the state space representation of the HMM. The reformulation involves deriving solutions to algebraic Riccati equations which are usually treated under minimality assumptions.

Publiceringsår

2002

Språk

Engelska

Publikation/Tidskrift/Serie

Preprints in Mathematical Sciences

Issue

2002:5

Dokumenttyp

Rapport

Förlag

Center for Mathematical Sceinces, Lund University

Ämne

  • Probability Theory and Statistics

Status

Published

Report number

LUTFMS-5019-2002