This thesis presents a comprehensive example
framework on how current multiple model state estimation algorithms for jump Markov linear systems can be improved. The possible improvements are categorized as:
-Design of multiple model state estimation algorithms using
new criteria.
-Improvements obtained using existing multiple model state
estimation algorithms.
In the first category, risk-sensitive estimation is proposed for
jump Markov linear systems. Two types of cost functions namely, the instantaneous and cumulative cost functions related with risk-sensitive estimation are examined and for each one, the
corresponding multiple model estate estimation algorithm is derived.
For the cumulative cost function, the derivation involves the
reference probability method where one defines and uses a new
probability measure under which the involved processes has
independence properties. The performance of the proposed
risk-sensitive filters are illustrated and compared with
conventional algorithms using simulations.
The thesis addresses the second category of improvements by
proposing
-Two new online transition probability estimation schemes for
jump Markov linear systems.
-A mixed multiple model state estimation scheme which combines
desirable properties of two different multiple model state
estimation methods.
The two online transition probability estimators proposed use the
recursive Kullback-Leibler (RKL) procedure and the maximum
likelihood (ML) criteria to derive the corresponding identification
schemes. When used in state estimation, these methods result in an average error decrease in the root mean square (RMS) state
estimation errors, which is proved using simulation studies.
The mixed multiple model estimation procedure
which utilizes the analysis of the single Gaussian approximation of Gaussian mixtures in Bayesian filtering, combines IMM (Interacting Multiple Model) filter and GPB2 (2nd Order Generalized Pseudo Bayesian) filter efficiently. The resulting algorithm reaches the performance of GPB2 with less Kalman filters.
| Identifer | oai:union.ndltd.org:METU/oai:etd.lib.metu.edu.tr:http://etd.lib.metu.edu.tr/upload/12607895/index.pdf |
| Date | 01 December 2006 |
| Creators | Orguner, Umut |
| Contributors | Demirekler, Mubeccel |
| Publisher | METU |
| Source Sets | Middle East Technical Univ. |
| Language | English |
| Detected Language | English |
| Type | Ph.D. Thesis |
| Format | text/pdf |
| Rights | To liberate the content for public access |
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