Information-based distributed extended Kalman filter with dynamic quantization via communication channels

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journalpeer-review

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Original languageEnglish
Pages (from-to)251-260
Journal / PublicationNeurocomputing
Online published22 Oct 2021
Publication statusPublished - 16 Jan 2022


This paper proposes a new information-based distributed extended Kalman filter algorithm under dynamic quantization. Our quantization framework has the advantage of utilizing online updated quantizer's parameters. A practical adjustment strategy is derived to ensure the availability of adaptive quantizer's parameters for the encoder and decoder. It is proved that estimation error of the proposed algorithm is exponentially bounded in mean square under some assumptions. A numerical example concerning target tracking is presented to demonstrate the validity of the main results, in which a complex network model is used to simulate the sensor network.

Research Area(s)

  • Distributed extended Kalman filter, Dynamic quantization, Information-based filtering, Sensor networks