G
Giorgio Battistelli
Researcher at University of Florence
Publications - 218
Citations - 5561
Giorgio Battistelli is an academic researcher from University of Florence. The author has contributed to research in topics: Linear system & Observability. The author has an hindex of 32, co-authored 203 publications receiving 4211 citations. Previous affiliations of Giorgio Battistelli include Leonardo & Chalmers University of Technology.
Papers
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Kullback-Leibler average, consensus on probability densities, and distributed state estimation with guaranteed stability
Giorgio Battistelli,Luigi Chisci +1 more
TL;DR: This paper addresses distributed state estimation over a sensor network wherein each node-equipped with processing, communication and sensing capabilities-repeatedly fuses local information with information from the neighbors, and derives a novel distributed state estimator.
Journal ArticleDOI
Consensus-Based Linear and Nonlinear Filtering
TL;DR: Novel theoretical results, limitedly to linear systems, on the guaranteed stability of the Hybrid CMCI filters under collective observability and network connectivity are proved.
Journal ArticleDOI
Consensus CPHD Filter for Distributed Multitarget Tracking
TL;DR: A novel consensus Gaussian Mixture-Cardinalized Probability Hypothesis Density filter is developed that provides a fully distributed, scalable and computationally efficient solution to the distributed multitarget tracking problem.
Journal ArticleDOI
Moving-horizon state estimation for nonlinear discrete-time systems: New stability results and approximation schemes
TL;DR: A generalized least-squares approach that consists in minimizing a quadratic estimation cost function defined on a recent batch of inputs and outputs according to a sliding-window strategy is used, and the existence of bounding sequences on the estimation error is proved.
Journal ArticleDOI
Stability of consensus extended Kalman filter for distributed state estimation
Giorgio Battistelli,Luigi Chisci +1 more
TL;DR: It is shown that the considered family of distributed Extended Kalman Filters enjoys local stability properties, under minimal requirements of network connectivity and system collective observability.