This article investigates the nonasymptotic and robust distributed algebraic stateestimation for linear time-varying systems using a network of sensors in noisy environments. First, at each sensor node, the system st...
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This article investigates the nonasymptotic and robust distributed algebraic stateestimation for linear time-varying systems using a network of sensors in noisy environments. First, at each sensor node, the system state is transformed into a linear combination of a group of nodes' local observable states, allowing for distributedestimation by estimating a reduced-order state for each node. Second, without requiring initial conditions, the estimation scheme based on generalized modulating functions is employed to estimate each node's local observable state variables through algebraic integral formulas, which are robust against corrupting noises. Subsequently, leveraging the obtained distributedstate expression and cross-agent communications with networked delays, a nonasymptotic and robust distributedstate estimator is designed. Furthermore, an estimation error bound in noisy cases is provided. Finally, two simulation examples are presented to illustrate the effectiveness of our proposed approach.
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