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

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 quan-tizer's parameters. A practical adjustment strategy is derived to ensure the availability of adaptive quan-tizer'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 con-cerning 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 . (c) 2021 Elsevier B.V. All rights reserved.

Automated summary

This paper proposes a new information-based distributed extended Kalman filter algorithm under dynamic quantization. It is proved that the estimation error of the proposed algorithm is exponentially bounded in mean square under certain assumptions. A numerical example concerning target tracking is presented to demonstrate the validity of the main results.