Bayesian estimation and the Kalman filter
| Autor: | Donald E. Brown, Allen L. Barker, Worthy N. Martin |
|---|---|
| Rok vydání: | 1995 |
| Předmět: |
Probabilistic Turing machine
business.industry Tracking Markov models Markov process Pattern recognition Dyanamic classification Kalman filter Bayesian statistics Markov model Turing machine Computational Mathematics symbols.namesake Extended Kalman filter Computational Theory and Mathematics Modelling and Simulation Modeling and Simulation symbols Artificial intelligence business Recursive Bayesian estimation Mathematics |
| Zdroj: | Computers & Mathematics with Applications. 30:55-77 |
| ISSN: | 0898-1221 |
| DOI: | 10.1016/0898-1221(95)00156-s |
| Popis: | In this tutorial article, we give a Bayesian derivation of a basic state estimation result for discrete-time Markov process models with independent process and measurement noise and measurements not affecting the state. We then list some properties of Gaussian random vectors and show how the Kalman filtering algorithm follows from the general state estimation result and a linear-Gaussian model definition. We give some illustrative examples including a probabilistic Turing machine, dynamic classification, and tracking a moving object. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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