- News
- Subject and Goals
- Council and Annual Reports
- Contacts and Working Groups
- Lectures and Presentations
- Conferences and Seminars
- Publications
- Application Demos
- International Travels
- Visitors
- Past Events
- E-Synergy Login
SEARCH
MEMBERS OF RESEARCH CENTER
Research Centre
Data - Algorithms - Decision Making
Data - Algorithms - Decision Making
Established in 2005 under support of MŠMT ČR (project 1M0572)
Publications
State estimation with missing data and bounded uncertainty
Typ:
Research report
Authors:
Name of edition:
Research Report
Article number:
2296
Publisher:
ÚTIA AV ČR, v.v.i
Serie:
Praha
Year:
2011
Keywords:
state-space model, filtering, bounded noise, incomplete data
Anotation:
The paper deals with two problems in the state estimation: (i) bounded uncertainty and (ii) missing measurement data. An algorithm for the state estimation of the discrete-time state space model whose uncertainties are bounded is proposed here. The algorithm also copes with situations when some data for identification are missing. The Bayesian approach is used and maximum a posteriori probability estimates are evaluated in the discrete time instants. The proposed estimation algorithm is applied to the estimation of vehicle position when incomplete data from global positioning system together with complete data from the inertial measurement unit are at disposal.