Kalman filtering

Dynamic state model
Kalman filtering

Author : Yves DELIGNON

Review date: February 17, 2026 | Lire en français

Add to my library

2. Dynamic state model

Let y₀,... y_n, be the observations up to time n of a system, where y_n can be scalars or vectors. Each observation y_n depends on a quantity x_n that defines the state of the system at time n. x_n is scalar or vector in nature and is related to observation y_n by a relationship of the type :

y_{n} = A_{n} (x_{n}, v_{n})

^( 5 )

where the vectors y_n and v_n are of dimension N while x_n is a vector of dimension M, A( )...

You do not have access to this resource.

Exclusive to subscribers. 97% yet to be discovered!

You do not have access to this resource. Click here to request your free trial access!

Already subscribed? Log in!

Ongoing reading
Dynamic state model

Previous
page Some elements of estimation

State process estimator

Bibliography

(1) - KALMAN (R.E.) - . – A New Approach to Linear Filtering and Prediction Problems, IEEE Transactions of the ASME – Journal of Basic Engineering, 82 (series D) : pp. 35-45 (1960).
(2) - KALMAN (R.E.), BUCY (R.S.) - . – New results in Linear Filtering and Prediction...

You do not have access to this resource.

Exclusive to subscribers. 97% yet to be discovered!

You do not have access to this resource. Click here to request your free trial access!

Already subscribed? Log in!

Article included in this offer

"Electronic measurements and tests"

( 80 articles )

Complete knowledge base

Updated and enriched with articles validated by our scientific committees

Services

A set of exclusive tools to complement the resources

View offer details

Dynamic state model Kalman filtering

2. Dynamic state model

Bibliography

Dynamic state model
Kalman filtering