Performance comparison of EKF and particle filtering methods for maneuvering targets
Digital Signal Processing
H∞ fuzzy filtering of nonlinear systems with intermittent measurements
IEEE Transactions on Fuzzy Systems
New approach to mixed H2/H∞ filtering for polytopic discrete-time systems
IEEE Transactions on Signal Processing - Part II
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements
IEEE Transactions on Signal Processing
A delay-dependent approach to robust H∞ filtering for uncertain discrete-time state-delayed systems
IEEE Transactions on Signal Processing
Automatica (Journal of IFAC)
Optimal recursive estimation with uncertain observation
IEEE Transactions on Information Theory
| Hi-index | 0.08 |
This paper is concerned with the robust filtering problem for a class of nonlinear stochastic systems with missing measurements and parameter uncertainties. The missing measurements are described by a binary switching sequence satisfying a conditional probability distribution, and the nonlinearities are expressed by the statistical means. The purpose of the filtering problem is to design a filter such that, for all admissible uncertainties and possible measurements missing, the dynamics of the filtering error is exponentially mean-square stable, and the individual steady-state error variance is not more than prescribed upper bound. A sufficient condition for the exponential mean-square stability of the filtering error system is first derived and an upper bound of the state estimation error variance is then obtained. In terms of certain linear matrix inequalities (LMIs), the solvability of the addressed problem is discussed and the explicit expression of the desired filters is also parameterized. Finally, a simulation example is provided to demonstrate the effectiveness and applicability of the proposed design approach.