Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Optimal State Estimation: Kalman, H Infinity, and Nonlinear Approaches
Brief paper: Optimal Kalman filtering fusion with cross-correlated sensor noises
Automatica (Journal of IFAC)
The Optimal Kalman Type State Estimator with Multi-Step Correlated Process and Measurement Noises
ICESS '08 Proceedings of the 2008 International Conference on Embedded Software and Systems
Optimal filtering for linear systems with state and multiple observation delays
International Journal of Systems Science
Brief paper: Robust filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
Recursive estimation of discrete-time signals from nonlinear randomly delayed observations
Computers & Mathematics with Applications
Kalman filtering with faded measurements
Automatica (Journal of IFAC)
Brief paper: H∞ filtering with randomly occurring sensor saturations and missing measurements
Automatica (Journal of IFAC)
Robust Kalman filters for linear time-varying systems withstochastic parametric uncertainties
IEEE Transactions on Signal Processing
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements
IEEE Transactions on Signal Processing
IEEE Transactions on Signal Processing - Part II
H∞ filtering for multiple-time-delay measurements
IEEE Transactions on Signal Processing
Brief paper: Optimal estimation of linear discrete-time systems with stochastic parameters
Automatica (Journal of IFAC)
Power Allocation for Outage Minimization in State Estimation Over Fading Channels
IEEE Transactions on Signal Processing
Extended Kalman filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
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This paper is concerned with the recursive filtering problem for a class of discrete-time nonlinear stochastic systems with random parameter matrices, multiple fading measurements and correlated noises. The phenomenon of measurement fading occurs in a random way and the fading probability for each sensor is governed by an individual random variable obeying a certain probability distribution over the known interval [@b"k,@c"k]. Such a probability distribution could be any commonly used discrete distribution over the interval [@b"k,@c"k] that covers the Bernoulli distribution as a special case. The process noise and the measurement noise are one-step autocorrelated, respectively. The process noise and the measurement noise are two-step cross-correlated. The purpose of the addressed filtering problem is to design an unbiased and recursive filter for the random parameter matrices, stochastic nonlinearity, and multiple fading measurements as well as correlated noises. Intensive stochastic analysis is carried out to obtain the filter gain characterized by the solution to a recursive matrix equation. The proposed scheme is of a form suitable for recursive computation in online applications. A simulation example is given to illustrate the effectiveness of the proposed filter design scheme.