The paper addresses distributed state estimation over a peer-to-peer sensor network with an eye to communica- tion/energy efficiency. In particular, consensus on exponential families of probability distributions is first introduced and shown to be equivalent to iteratively performing convex linear combinations on the natural parameters of such distributions. Then, an event-triggered consensus strategy is presented and exploited to derive a novel energy-efficient consensus Kalman filter algorithm for distributed state estimation. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.

Energy-efficient distributed state estimation via event-triggered consensus on exponential families

Selvi Daniela
2016-01-01

Abstract

The paper addresses distributed state estimation over a peer-to-peer sensor network with an eye to communica- tion/energy efficiency. In particular, consensus on exponential families of probability distributions is first introduced and shown to be equivalent to iteratively performing convex linear combinations on the natural parameters of such distributions. Then, an event-triggered consensus strategy is presented and exploited to derive a novel energy-efficient consensus Kalman filter algorithm for distributed state estimation. Simulation results are provided to demonstrate the effectiveness of the proposed algorithm.
2016
Distributed state estimation. Event-triggered consensus. Kalman filter. Exponential families.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.11771/7027
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