Introduces a DUIO framework that combines local state reconstruction with distributed optimization to achieve bounded-error estimation in discrete-time systems with unknown inputs.
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Derives explicit formula for causally conditioned directed information rate of Gaussian sequences based on optimal prediction and proves O(N^{-1/2} log N) high-probability error bound for the resulting estimator.
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Distributed State Estimation for Discrete-Time Systems With Unknown Inputs: An Optimization Approach
Introduces a DUIO framework that combines local state reconstruction with distributed optimization to achieve bounded-error estimation in discrete-time systems with unknown inputs.
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Non-Asymptotic Error Bounds for Causally Conditioned Directed Information Rates of Gaussian Sequences
Derives explicit formula for causally conditioned directed information rate of Gaussian sequences based on optimal prediction and proves O(N^{-1/2} log N) high-probability error bound for the resulting estimator.