Polynomial filtering of systems with non-independent uncertain observations
- Resource Type
- Conference
- Authors
- Carravetta, F.; Mavelli, G.
- Source
- 2004 43rd IEEE Conference on Decision and Control (CDC) (IEEE Cat. No.04CH37601) Decision and control Decision and Control, 2004. CDC. 43rd IEEE Conference on. 3:3109-3114 2004
- Subject
- Robotics and Control Systems
Computing and Processing
Polynomials
Filtering
Nonlinear filters
Recursive estimation
Uncertainty
State estimation
Equations
Random variables
Remote sensing
Statistics
- Language
- ISSN
- 0191-2216
The filtering problem for non-Gaussian, discretetime, linear systems with correlated uncertainty in the observation equation is investigated in the present paper. A stochastic Markov sequence of correlated Bernoulli random variables is considered as a model for the uncertainty in the measurements. For this class of systems Hadidi-Schwartz defined a linear filter (giving the linear-optimal state estimate) assuming some structural properties of the system are satisfied. In the present paper similar conditions are shown to imply the existence of a polynomial filter (of any degree). Finally, the general polynomial filter equations are derived for the considered class of systems.