Stability of Kalman filtering with Markovian packet losses
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Optimal control of LTI systems over unreliable communication links
Automatica (Journal of IFAC)
Brief paper: Optimal linear estimation for systems with multiple packet dropouts
Automatica (Journal of IFAC)
Brief paper: Observer-based networked control for continuous-time systems with random sensor delays
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Brief paper: Robust filtering with stochastic nonlinearities and multiple missing measurements
Automatica (Journal of IFAC)
Brief paper: Robust sampled-data H∞ control with stochastic sampling
Automatica (Journal of IFAC)
Brief paper: Finite-state, discrete-time optimization with randomly varying observation quality
Automatica (Journal of IFAC)
H∞ filtering with stochastic sampling
Signal Processing
Fixed-interval smoothing algorithm based on covariances with correlation in the uncertainty
Digital Signal Processing
On estimation of discrete processes under multiplicative and additive noise conditions
Information Sciences: an International Journal
Journal of Computational and Applied Mathematics
Derivation of centralized and distributed filters using covariance information
Computational Statistics & Data Analysis
Paper: A survey of design methods for failure detection in dynamic systems
Automatica (Journal of IFAC)
Brief paper: Random sampling approach to state estimation in switching environments
Automatica (Journal of IFAC)
Brief paper: Optimal estimation of linear discrete-time systems with stochastic parameters
Automatica (Journal of IFAC)
On identification and adaptive estimation for systems with interrupted observations
Automatica (Journal of IFAC)
Automatica (Journal of IFAC)
Optimal linear state estimation over a packet-dropping network using linear temporal coding
Automatica (Journal of IFAC)
Hi-index | 754.87 |
In classical estimation theory, the observation is always assumed to contain the signal to be estimated. In practice, certain observations, or sequences of observations, may contain noise alone, only the probability of occurrence of such cases being available to the estimator. An example is trajectory tracking where the signal is first detected and then the estimator is allowed to process it for tracking purposes. However, any detection decision is associated with a false-alarm probability, which is the probability that the detected signal contains only noise. Minimum mean-square estimators are derived for two different forms of this problem; 1) when it is possible that the observation at any sample time contains signal or is noise alone, independent of the situation at any other sample, and 2) when the entire sequence of observations contains signal or is only noise. The estimators derived are of recursive form. A simple example is given for illustration.