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Distributed Kalman Filtering With Adaptive Communication
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NSTL
IEEE
This letter proposes an adaptive event-triggered communication framework for distributed state estimation in sensor networks, enabling each node to self-adapt its transmission rule while maintaining a desired average rate and complying with an upper bound on individual transmission rates. Unlike existing approaches that require manual tuning, the proposed method dynamically tunes transmission thresholds, achieving uniform energy and bandwidth consumption across nodes. We analyze the theoretical properties of the proposed transmission strategy, and verify its effectiveness through simulations in a target-tracking scenario.
Kalman filtersCovariance matricesState estimationBandwidthWindowsUpper boundProbability density functionAdaptive systemsVectorsTuning
Daniela Selvi、Giorgio Battistelli
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Dipartimento di Ingegneria dell’Informazione, and the Centro di Ricerca “Enrico Piaggio”, Università di Pisa, Pisa, Italy
Dipartimento di Ingegneria dell’Informazione, Università di Firenze, Florence, Italy
NETL
NSTL
IEEE
