Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
Decentralized Estimation and Control for Multisensor Systems
Decentralized Estimation and Control for Multisensor Systems
Mathematical Techniques in Multisensor Data Fusion
Mathematical Techniques in Multisensor Data Fusion
Optimal centralized update with multiple local out-of-sequence measurements
IEEE Transactions on Signal Processing
Optimal update with out-of-sequence measurements
IEEE Transactions on Signal Processing
Kalman filtering for multiple time-delay systems
Automatica (Journal of IFAC)
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This paper presents a set of new centralized algorithms for estimating the state of linear dynamic Multiple-Input Multiple-Output (MIMO) control systems with asynchronous, non-systematically delayed and corrupted measurements provided by a set of sensors. The delays, which make the data available Out-Of-Sequence (OOS), appear when using physically distributed sensors, communication networks and pre-processing algorithms. The potentially corrupted measurements can be generated by malfunctioning sensors or communication errors. Our algorithms, designed to work with real-time control systems, handle these problems with a streamlined memory and computational efficient reorganization of the basic operations of the Kalman and Information Filters (KF & IF). The two versions designed to deal only with valid measurements are optimal solutions of the OOS problem, while the other two remaining are suboptimal algorithms able to handle corrupted data.