A distributed ADMM-based Kalman-like observer with sparsity-preserving prediction achieves uniform exponential stability for cooperative localization in multi-agent systems.
Distributed optimization and statistical learning via the alternating direction method of multipliers
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A data-driven moving horizon estimator is proposed for linear systems with unknown parameters, proving that its expected estimation error is ultimately bounded and relating the bound to noise covariances and offline data length.
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ADMM-Based Distributed Kalman-like Observer with Applications to Cooperative Localization
A distributed ADMM-based Kalman-like observer with sparsity-preserving prediction achieves uniform exponential stability for cooperative localization in multi-agent systems.
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Data-Driven Moving Horizon Estimators for Linear Systems with Sample Complexity Analysis
A data-driven moving horizon estimator is proposed for linear systems with unknown parameters, proving that its expected estimation error is ultimately bounded and relating the bound to noise covariances and offline data length.