Kalman filtering with real-time applications
Kalman filtering with real-time applications
Stochastic analysis and control of real-time systems with random time delays
Automatica (Journal of IFAC)
Quadratic Estimation of Multivariate Signals from Randomly Delayed Measurements*
Multidimensional Systems and Signal Processing
Approximation of Nonlinear Filters for Markov Systems with Delayed Observations
SIAM Journal on Control and Optimization
Least-Squares Linear Smoothers from Randomly Delayed Observations with Correlation in the Delay
IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences
Filtering for a class of nonlinear discrete-time stochastic systems with state delays
Journal of Computational and Applied Mathematics
Robust filtering with randomly varying sensor delay: the finite-horizon case
IEEE Transactions on Circuits and Systems Part I: Regular Papers
Kalman filtering for multiple time-delay systems
Automatica (Journal of IFAC)
Decentralized robust set-valued state estimation in networked multiple sensor systems
Computers & Mathematics with Applications
Digital Signal Processing
Automatica (Journal of IFAC)
Hi-index | 0.09 |
In this paper, one-stage prediction, filtering, and fixed-point smoothing problems are addressed for nonlinear discrete-time stochastic systems with randomly delayed measurements perturbed by additive white noise. The observation delay is modelled by a sequence of independent Bernoulli random variables whose values-zero or one-indicate that the real observation arrives on time or it is delayed one sampling time and, hence, the available measurement to estimate the signal is not updated. Assuming that the state-space model generating the signal to be estimated is unknown and only the covariance functions of the processes involved in the observation equation are available, recursive estimation algorithms based on linear approximations of the real observations are proposed.