Linear recursive discrete-time estimators using covariance information under uncertain observations
Signal Processing - From signal processing theory to implementation
New recursive estimators from correlated interrupted observations using covariance information
International Journal of Systems Science
Automatica (Journal of IFAC)
Incorporating Data from Multiple Sensors for Localizing Nodes in Mobile Ad Hoc Networks
IEEE Transactions on Mobile Computing
Fixed-interval smoothing algorithm based on covariances with correlation in the uncertainty
Digital Signal Processing
Robust H/sub /spl infin// filtering for stochastic time-delay systems with missing measurements
IEEE Transactions on Signal Processing
ISCGAV'09 Proceedings of the 9th WSEAS international conference on Signal processing, computational geometry and artificial vision
IEEE Transactions on Signal Processing
Derivation of centralized and distributed filters using covariance information
Computational Statistics & Data Analysis
IEEE Transactions on Signal Processing
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The linear least-squares estimation problem of signals from observations coming from multiple sensors is addressed when there is a non-zero probability that each observation does not contain the signal to be estimated (uncertain observations). At each sensor, this uncertainty in the observations is modeled by a sequence of Bernoulli random variables correlated at consecutive sampling times. To estimate the signal, recursive filtering and (fixed-point and fixed-interval) smoothing algorithms are derived without requiring the knowledge of the signal state-space model but only the means and covariance functions of the processes involved in the observation equations, the uncertainty probabilities and the correlation between the variables modeling the uncertainty. To measure the estimation accuracy, recursive expressions for the estimation error covariance matrices are also proposed. The theoretical results are illustrated by a numerical simulation example where a signal is estimated from observations featuring correlated uncertainty and coming from two sensors with different uncertainty characteristics.